From 04347b296dfbb520e3c713f4d40bbb622a93c0aa Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 14 Nov 2023 19:47:55 +0100
Subject: [PATCH 01/64] Delete node locally in gRPC-rere (#2596)

---
 src/py/flwr/client/grpc_rere_client/connection.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/py/flwr/client/grpc_rere_client/connection.py b/src/py/flwr/client/grpc_rere_client/connection.py
index 3dcc147e8eca..b69228826e13 100644
--- a/src/py/flwr/client/grpc_rere_client/connection.py
+++ b/src/py/flwr/client/grpc_rere_client/connection.py
@@ -136,6 +136,8 @@ def delete_node() -> None:
         delete_node_request = DeleteNodeRequest(node=node)
         stub.DeleteNode(request=delete_node_request)
 
+        del node_store[KEY_NODE]
+
     def receive() -> Optional[TaskIns]:
         """Receive next task from server."""
         # Get Node

From e2116b051ff852b340e2a7913a969e551f020145 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 14 Nov 2023 19:52:52 +0100
Subject: [PATCH 02/64] C++ SDK: Delete local node for gRPC-rere (#2597)

---
 src/cc/flwr/src/grpc_rere.cc | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/src/cc/flwr/src/grpc_rere.cc b/src/cc/flwr/src/grpc_rere.cc
index c4920e986b1b..267874a7a0e2 100644
--- a/src/cc/flwr/src/grpc_rere.cc
+++ b/src/cc/flwr/src/grpc_rere.cc
@@ -19,6 +19,14 @@ std::optional<flwr::proto::Node> get_node_from_store() {
   return node->second;
 }
 
+void delete_node_from_store() {
+  std::lock_guard<std::mutex> lock(node_store_mutex);
+  auto node = node_store.find(KEY_NODE);
+  if (node == node_store.end() || !node->second.has_value()) {
+    node_store.erase(node);
+  }
+}
+
 std::optional<flwr::proto::TaskIns> get_current_task_ins() {
   std::lock_guard<std::mutex> state_lock(state_mutex);
   auto current_task_ins = state.find(KEY_TASK_INS);
@@ -80,8 +88,7 @@ void delete_node(const std::unique_ptr<flwr::proto::Fleet::Stub> &stub) {
     delete_node_request.release_node(); // Release if status is ok
   }
 
-  // TODO: Check if Node needs to be removed from local map
-  // node_store.erase(node);
+  delete_node_from_store();
 }
 
 std::optional<flwr::proto::TaskIns>

From db38b94d09d0d77e96fe99ecbe57db2a9999738a Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 10:46:14 +0100
Subject: [PATCH 03/64] Change the settings for IidPartitioner (#2589)

---
 datasets/flwr_datasets/partitioner/iid_partitioner.py  |  2 +-
 .../flwr_datasets/partitioner/iid_partitioner_test.py  | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/datasets/flwr_datasets/partitioner/iid_partitioner.py b/datasets/flwr_datasets/partitioner/iid_partitioner.py
index 37b97468cadf..c8dbf8294fec 100644
--- a/datasets/flwr_datasets/partitioner/iid_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/iid_partitioner.py
@@ -48,5 +48,5 @@ def load_partition(self, idx: int) -> datasets.Dataset:
             single dataset partition
         """
         return self.dataset.shard(
-            num_shards=self._num_partitions, index=idx, contiguous=False
+            num_shards=self._num_partitions, index=idx, contiguous=True
         )
diff --git a/datasets/flwr_datasets/partitioner/iid_partitioner_test.py b/datasets/flwr_datasets/partitioner/iid_partitioner_test.py
index 5f851807f4bd..64c37c4e7127 100644
--- a/datasets/flwr_datasets/partitioner/iid_partitioner_test.py
+++ b/datasets/flwr_datasets/partitioner/iid_partitioner_test.py
@@ -18,7 +18,6 @@
 import unittest
 from typing import Tuple
 
-import numpy as np
 from parameterized import parameterized
 
 from datasets import Dataset
@@ -102,14 +101,15 @@ def test_load_partition_correct_data(
     ) -> None:
         """Test if the data in partition is equal to the expected."""
         dataset, partitioner = _dummy_setup(num_partitions, num_rows)
+        partition_size = num_rows // num_partitions
         partition_index = 2
         partition = partitioner.load_partition(partition_index)
         row_id = 0
         self.assertEqual(
-            partition["features"][row_id],
-            dataset[np.arange(partition_index, len(dataset), num_partitions)][
-                "features"
-            ][row_id],
+            partition[row_id]["features"],
+            # Note it's contiguous so partition_size * partition_index gets the first
+            # element of the partition of partition_index
+            dataset[partition_size * partition_index + row_id]["features"],
         )
 
     @parameterized.expand(  # type: ignore

From d00a579d01e4a254c871b6b0594b3690e0c92ad7 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 11:49:38 +0100
Subject: [PATCH 04/64] Fds fix missing __all__ in inits (#2599)

---
 datasets/flwr_datasets/__init__.py            | 10 ++++++---
 datasets/flwr_datasets/resplitter/__init__.py | 22 +++++++++++++++++++
 .../{ => resplitter}/merge_resplitter.py      |  0
 .../{ => resplitter}/merge_resplitter_test.py |  2 +-
 datasets/flwr_datasets/utils.py               |  2 +-
 5 files changed, 31 insertions(+), 5 deletions(-)
 create mode 100644 datasets/flwr_datasets/resplitter/__init__.py
 rename datasets/flwr_datasets/{ => resplitter}/merge_resplitter.py (100%)
 rename datasets/flwr_datasets/{ => resplitter}/merge_resplitter_test.py (98%)

diff --git a/datasets/flwr_datasets/__init__.py b/datasets/flwr_datasets/__init__.py
index 89e651e0f558..48d993037708 100644
--- a/datasets/flwr_datasets/__init__.py
+++ b/datasets/flwr_datasets/__init__.py
@@ -15,10 +15,14 @@
 """Flower Datasets main package."""
 
 
+from flwr_datasets import partitioner, resplitter
 from flwr_datasets.common.version import package_version as _package_version
+from flwr_datasets.federated_dataset import FederatedDataset
 
-from .federated_dataset import FederatedDataset
-
-__all__ = ["FederatedDataset"]
+__all__ = [
+    "FederatedDataset",
+    "partitioner",
+    "resplitter",
+]
 
 __version__ = _package_version
diff --git a/datasets/flwr_datasets/resplitter/__init__.py b/datasets/flwr_datasets/resplitter/__init__.py
new file mode 100644
index 000000000000..f778d2096b76
--- /dev/null
+++ b/datasets/flwr_datasets/resplitter/__init__.py
@@ -0,0 +1,22 @@
+# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Resplitter package."""
+
+
+from .merge_resplitter import MergeResplitter
+
+__all__ = [
+    "MergeResplitter",
+]
diff --git a/datasets/flwr_datasets/merge_resplitter.py b/datasets/flwr_datasets/resplitter/merge_resplitter.py
similarity index 100%
rename from datasets/flwr_datasets/merge_resplitter.py
rename to datasets/flwr_datasets/resplitter/merge_resplitter.py
diff --git a/datasets/flwr_datasets/merge_resplitter_test.py b/datasets/flwr_datasets/resplitter/merge_resplitter_test.py
similarity index 98%
rename from datasets/flwr_datasets/merge_resplitter_test.py
rename to datasets/flwr_datasets/resplitter/merge_resplitter_test.py
index 096bd7efac27..ebbdfb4022b0 100644
--- a/datasets/flwr_datasets/merge_resplitter_test.py
+++ b/datasets/flwr_datasets/resplitter/merge_resplitter_test.py
@@ -21,7 +21,7 @@
 import pytest
 
 from datasets import Dataset, DatasetDict
-from flwr_datasets.merge_resplitter import MergeResplitter
+from flwr_datasets.resplitter.merge_resplitter import MergeResplitter
 
 
 class TestResplitter(unittest.TestCase):
diff --git a/datasets/flwr_datasets/utils.py b/datasets/flwr_datasets/utils.py
index 7badb1445460..49c65e9893a7 100644
--- a/datasets/flwr_datasets/utils.py
+++ b/datasets/flwr_datasets/utils.py
@@ -19,8 +19,8 @@
 from typing import Dict, Optional, Tuple, Union, cast
 
 from flwr_datasets.common import Resplitter
-from flwr_datasets.merge_resplitter import MergeResplitter
 from flwr_datasets.partitioner import IidPartitioner, Partitioner
+from flwr_datasets.resplitter.merge_resplitter import MergeResplitter
 
 tested_datasets = [
     "mnist",

From 04e2ea3f085b8eb005f8ec4e4a6879fd52de97ed Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 13:52:32 +0100
Subject: [PATCH 05/64] Add shuffle seed parameters to FederatedDataset (#2588)

---
 datasets/flwr_datasets/federated_dataset.py   | 86 +++++++++++-------
 .../flwr_datasets/federated_dataset_test.py   | 89 ++++++++++++++++++-
 2 files changed, 144 insertions(+), 31 deletions(-)

diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py
index 6e4e9ca43ccb..b1e61f1f9231 100644
--- a/datasets/flwr_datasets/federated_dataset.py
+++ b/datasets/flwr_datasets/federated_dataset.py
@@ -38,18 +38,25 @@ class FederatedDataset:
 
     Parameters
     ----------
-    dataset: str
+    dataset : str
         The name of the dataset in the Hugging Face Hub.
-    subset: str
+    subset : str
         Secondary information regarding the dataset, most often subset or version
         (that is passed to the name in datasets.load_dataset).
-    resplitter: Optional[Union[Resplitter, Dict[str, Tuple[str, ...]]]]
+    resplitter : Optional[Union[Resplitter, Dict[str, Tuple[str, ...]]]]
         `Callable` that transforms `DatasetDict` splits, or configuration dict for
         `MergeResplitter`.
-    partitioners: Dict[str, Union[Partitioner, int]]
+    partitioners : Dict[str, Union[Partitioner, int]]
         A dictionary mapping the Dataset split (a `str`) to a `Partitioner` or an `int`
         (representing the number of IID partitions that this split should be partitioned
         into).
+    shuffle : bool
+        Whether to randomize the order of samples. Applied prior to resplitting,
+        speratelly to each of the present splits in the dataset. It uses the `seed`
+        argument. Defaults to True.
+    seed : Optional[int]
+        Seed used for dataset shuffling. It has no effect if `shuffle` is False. The
+        seed cannot be set in the later stages.
 
     Examples
     --------
@@ -66,6 +73,7 @@ class FederatedDataset:
     >>> centralized = mnist_fds.load_full("test")
     """
 
+    # pylint: disable=too-many-instance-attributes
     def __init__(
         self,
         *,
@@ -73,6 +81,8 @@ def __init__(
         subset: Optional[str] = None,
         resplitter: Optional[Union[Resplitter, Dict[str, Tuple[str, ...]]]] = None,
         partitioners: Dict[str, Union[Partitioner, int]],
+        shuffle: bool = True,
+        seed: Optional[int] = 42,
     ) -> None:
         _check_if_dataset_tested(dataset)
         self._dataset_name: str = dataset
@@ -83,9 +93,13 @@ def __init__(
         self._partitioners: Dict[str, Partitioner] = _instantiate_partitioners(
             partitioners
         )
-        #  Init (download) lazily on the first call to `load_partition` or `load_full`
+        self._shuffle = shuffle
+        self._seed = seed
+        #  _dataset is prepared lazily on the first call to `load_partition`
+        #  or `load_full`. See _prepare_datasets for more details
         self._dataset: Optional[DatasetDict] = None
-        self._resplit: bool = False  # Indicate if the resplit happened
+        # Indicate if the dataset is prepared for `load_partition` or `load_full`
+        self._dataset_prepared: bool = False
 
     def load_partition(self, idx: int, split: str) -> Dataset:
         """Load the partition specified by the idx in the selected split.
@@ -95,9 +109,9 @@ def load_partition(self, idx: int, split: str) -> Dataset:
 
         Parameters
         ----------
-        idx: int
+        idx : int
             Partition index for the selected split, idx in {0, ..., num_partitions - 1}.
-        split: str
+        split : str
             Name of the (partitioned) split (e.g. "train", "test").
 
         Returns
@@ -105,8 +119,8 @@ def load_partition(self, idx: int, split: str) -> Dataset:
         partition: Dataset
             Single partition from the dataset split.
         """
-        self._download_dataset_if_none()
-        self._resplit_dataset_if_needed()
+        if not self._dataset_prepared:
+            self._prepare_dataset()
         if self._dataset is None:
             raise ValueError("Dataset is not loaded yet.")
         self._check_if_split_present(split)
@@ -123,7 +137,7 @@ def load_full(self, split: str) -> Dataset:
 
         Parameters
         ----------
-        split: str
+        split : str
             Split name of the downloaded dataset (e.g. "train", "test").
 
         Returns
@@ -131,20 +145,13 @@ def load_full(self, split: str) -> Dataset:
         dataset_split: Dataset
             Part of the dataset identified by its split name.
         """
-        self._download_dataset_if_none()
-        self._resplit_dataset_if_needed()
+        if not self._dataset_prepared:
+            self._prepare_dataset()
         if self._dataset is None:
             raise ValueError("Dataset is not loaded yet.")
         self._check_if_split_present(split)
         return self._dataset[split]
 
-    def _download_dataset_if_none(self) -> None:
-        """Lazily load (and potentially download) the Dataset instance into memory."""
-        if self._dataset is None:
-            self._dataset = datasets.load_dataset(
-                path=self._dataset_name, name=self._subset
-            )
-
     def _check_if_split_present(self, split: str) -> None:
         """Check if the split (for partitioning or full return) is in the dataset."""
         if self._dataset is None:
@@ -176,15 +183,34 @@ def _assign_dataset_to_partitioner(self, split: str) -> None:
         if not self._partitioners[split].is_dataset_assigned():
             self._partitioners[split].dataset = self._dataset[split]
 
-    def _resplit_dataset_if_needed(self) -> None:
-        # The actual re-splitting can't be done more than once.
-        # The attribute `_resplit` indicates that the resplit happened.
-
-        # Resplit only once
-        if self._resplit:
-            return
-        if self._dataset is None:
-            raise ValueError("The dataset resplit should happen after the download.")
+    def _prepare_dataset(self) -> None:
+        """Prepare the dataset (prior to partitioning) by download, shuffle, replit.
+
+        Run only ONCE when triggered by load_* function. (In future more control whether
+        this should happen lazily or not can be added). The operations done here should
+        not happen more than once.
+
+        It is controlled by a single flag, `_dataset_prepared` that is set True at the
+        end of the function.
+
+        Notes
+        -----
+        The shuffling should happen before the resplitting. Here is the explanation.
+        If the dataset has a non-random order of samples e.g. each split has first
+        only label 0, then only label 1. Then in case of resplitting e.g.
+        someone creates: "train" train[:int(0.75 * len(train))], test: concat(
+        train[int(0.75 * len(train)):], test). The new test took the 0.25 of e.g.
+        the train that is only label 0 (assuming the equal count of labels).
+        Therefore, for such edge cases (for which we have split) the split should
+        happen before the resplitting.
+        """
+        self._dataset = datasets.load_dataset(
+            path=self._dataset_name, name=self._subset
+        )
+        if self._shuffle:
+            # Note it shuffles all the splits. The self._dataset is DatasetDict
+            # so e.g. {"train": train_data, "test": test_data}. All splits get shuffled.
+            self._dataset = self._dataset.shuffle(seed=self._seed)
         if self._resplitter:
             self._dataset = self._resplitter(self._dataset)
-        self._resplit = True
+        self._dataset_prepared = True
diff --git a/datasets/flwr_datasets/federated_dataset_test.py b/datasets/flwr_datasets/federated_dataset_test.py
index ca2bc97a33ee..1e36fd565d06 100644
--- a/datasets/flwr_datasets/federated_dataset_test.py
+++ b/datasets/flwr_datasets/federated_dataset_test.py
@@ -18,12 +18,13 @@
 
 import unittest
 from typing import Dict, Union
+from unittest.mock import Mock, patch
 
 import pytest
 from parameterized import parameterized, parameterized_class
 
 import datasets
-from datasets import DatasetDict, concatenate_datasets
+from datasets import Dataset, DatasetDict, concatenate_datasets
 from flwr_datasets.federated_dataset import FederatedDataset
 from flwr_datasets.partitioner import IidPartitioner, Partitioner
 
@@ -144,6 +145,92 @@ def resplit(dataset: DatasetDict) -> DatasetDict:
         self.assertEqual(len(full), dataset_length)
 
 
+class ArtificialDatasetTest(unittest.TestCase):
+    """Test using small artificial dataset, mocked load_dataset."""
+
+    # pylint: disable=no-self-use
+    def _dummy_setup(self, train_rows: int = 10, test_rows: int = 5) -> DatasetDict:
+        """Create a dummy DatasetDict with train, test splits."""
+        data_train = {
+            "features": list(range(train_rows)),
+            "labels": list(range(100, 100 + train_rows)),
+        }
+        data_test = {
+            "features": [200] + [201] * (test_rows - 1),
+            "labels": [202] + [203] * (test_rows - 1),
+        }
+        train_dataset = Dataset.from_dict(data_train)
+        test_dataset = Dataset.from_dict(data_test)
+        return DatasetDict({"train": train_dataset, "test": test_dataset})
+
+    @patch("datasets.load_dataset")
+    def test_shuffling_applied(self, mock_func: Mock) -> None:
+        """Test if argument is used."""
+        dummy_ds = self._dummy_setup()
+        mock_func.return_value = dummy_ds
+
+        expected_result = dummy_ds.shuffle(seed=42)["train"]["features"]
+        fds = FederatedDataset(
+            dataset="does-not-matter", partitioners={"train": 10}, shuffle=True, seed=42
+        )
+        train = fds.load_full("train")
+        # This should be shuffled
+        result = train["features"]
+
+        self.assertEqual(expected_result, result)
+
+    @patch("datasets.load_dataset")
+    def test_shuffling_not_applied(self, mock_func: Mock) -> None:
+        """Test if argument is not used."""
+        dummy_ds = self._dummy_setup()
+        mock_func.return_value = dummy_ds
+
+        expected_result = dummy_ds["train"]["features"]
+        fds = FederatedDataset(
+            dataset="does-not-matter",
+            partitioners={"train": 10},
+            shuffle=False,
+        )
+        train = fds.load_full("train")
+        # This should not be shuffled
+        result = train["features"]
+
+        self.assertEqual(expected_result, result)
+
+    @patch("datasets.load_dataset")
+    def test_shuffling_before_to_resplitting_applied(self, mock_func: Mock) -> None:
+        """Check if the order is met and if the shuffling happens."""
+
+        def resplit(dataset: DatasetDict) -> DatasetDict:
+            #  "Move" the last sample from test to train
+            return DatasetDict(
+                {
+                    "train": concatenate_datasets(
+                        [dataset["train"], dataset["test"].select([0])]
+                    ),
+                    "test": dataset["test"].select(range(1, dataset["test"].num_rows)),
+                }
+            )
+
+        dummy_ds = self._dummy_setup()
+        mock_func.return_value = dummy_ds
+
+        expected_result = concatenate_datasets(
+            [dummy_ds["train"].shuffle(42), dummy_ds["test"].shuffle(42).select([0])]
+        )["features"]
+        fds = FederatedDataset(
+            dataset="does-not-matter",
+            partitioners={"train": 10},
+            resplitter=resplit,
+            shuffle=True,
+        )
+        train = fds.load_full("train")
+        # This should not be shuffled
+        result = train["features"]
+
+        self.assertEqual(expected_result, result)
+
+
 class PartitionersSpecificationForFederatedDatasets(unittest.TestCase):
     """Test the specifications of partitioners for `FederatedDataset`."""
 

From 3c8975476e0201fe75a1aeed46a23cbb8897d1d1 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 14:22:08 +0100
Subject: [PATCH 06/64] Make the split keyword optional for load_partition
 (#2423)

---
 datasets/flwr_datasets/federated_dataset.py   | 25 ++++++++++++++----
 .../flwr_datasets/federated_dataset_test.py   | 26 +++++++++++++++++++
 2 files changed, 46 insertions(+), 5 deletions(-)

diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py
index b1e61f1f9231..52acc4d40100 100644
--- a/datasets/flwr_datasets/federated_dataset.py
+++ b/datasets/flwr_datasets/federated_dataset.py
@@ -101,7 +101,7 @@ def __init__(
         # Indicate if the dataset is prepared for `load_partition` or `load_full`
         self._dataset_prepared: bool = False
 
-    def load_partition(self, idx: int, split: str) -> Dataset:
+    def load_partition(self, idx: int, split: Optional[str] = None) -> Dataset:
         """Load the partition specified by the idx in the selected split.
 
         The dataset is downloaded only when the first call to `load_partition` or
@@ -111,18 +111,26 @@ def load_partition(self, idx: int, split: str) -> Dataset:
         ----------
         idx : int
             Partition index for the selected split, idx in {0, ..., num_partitions - 1}.
-        split : str
-            Name of the (partitioned) split (e.g. "train", "test").
+        split : Optional[str]
+            Name of the (partitioned) split (e.g. "train", "test"). You can skip this
+            parameter if there is only one partitioner for the dataset. The name will be
+            inferred automatically. For example, if `partitioners={"train": 10}`, you do
+            not need to provide this argument, but if `partitioners={"train": 10,
+            "test": 100}`, you need to set it to differentiate which partitioner should
+            be used.
 
         Returns
         -------
-        partition: Dataset
+        partition : Dataset
             Single partition from the dataset split.
         """
         if not self._dataset_prepared:
             self._prepare_dataset()
         if self._dataset is None:
             raise ValueError("Dataset is not loaded yet.")
+        if split is None:
+            self._check_if_no_split_keyword_possible()
+            split = list(self._partitioners.keys())[0]
         self._check_if_split_present(split)
         self._check_if_split_possible_to_federate(split)
         partitioner: Partitioner = self._partitioners[split]
@@ -142,7 +150,7 @@ def load_full(self, split: str) -> Dataset:
 
         Returns
         -------
-        dataset_split: Dataset
+        dataset_split : Dataset
             Part of the dataset identified by its split name.
         """
         if not self._dataset_prepared:
@@ -214,3 +222,10 @@ def _prepare_dataset(self) -> None:
         if self._resplitter:
             self._dataset = self._resplitter(self._dataset)
         self._dataset_prepared = True
+
+    def _check_if_no_split_keyword_possible(self) -> None:
+        if len(self._partitioners) != 1:
+            raise ValueError(
+                "Please set the `split` argument. You can only omit the split keyword "
+                "if there is exactly one partitioner specified."
+            )
diff --git a/datasets/flwr_datasets/federated_dataset_test.py b/datasets/flwr_datasets/federated_dataset_test.py
index 1e36fd565d06..e02b6ed5add8 100644
--- a/datasets/flwr_datasets/federated_dataset_test.py
+++ b/datasets/flwr_datasets/federated_dataset_test.py
@@ -95,6 +95,18 @@ def test_multiple_partitioners(self) -> None:
             len(dataset[self.test_split]) // num_test_partitions,
         )
 
+    def test_no_need_for_split_keyword_if_one_partitioner(self) -> None:
+        """Test if partitions got with and without split args are the same."""
+        fds = FederatedDataset(dataset="mnist", partitioners={"train": 10})
+        partition_loaded_with_no_split_arg = fds.load_partition(0)
+        partition_loaded_with_verbose_split_arg = fds.load_partition(0, "train")
+        self.assertTrue(
+            datasets_are_equal(
+                partition_loaded_with_no_split_arg,
+                partition_loaded_with_verbose_split_arg,
+            )
+        )
+
     def test_resplit_dataset_into_one(self) -> None:
         """Test resplit into a single dataset."""
         dataset = datasets.load_dataset(self.dataset_name)
@@ -340,5 +352,19 @@ def test_cannot_use_the_old_split_names(self) -> None:
             fds.load_partition(0, "train")
 
 
+def datasets_are_equal(ds1: Dataset, ds2: Dataset) -> bool:
+    """Check if two Datasets have the same values."""
+    # Check if both datasets have the same length
+    if len(ds1) != len(ds2):
+        return False
+
+    # Iterate over each row and check for equality
+    for row1, row2 in zip(ds1, ds2):
+        if row1 != row2:
+            return False
+
+    return True
+
+
 if __name__ == "__main__":
     unittest.main()

From 93b8a3dc2df6243dd9e395721a2a42f60516f8ae Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 15:05:30 +0100
Subject: [PATCH 07/64] Rename idx to node_id (#2590)

---
 datasets/README.md                                        | 2 +-
 datasets/flwr_datasets/federated_dataset.py               | 6 +++---
 .../flwr_datasets/partitioner/exponential_partitioner.py  | 4 ++--
 datasets/flwr_datasets/partitioner/iid_partitioner.py     | 6 +++---
 datasets/flwr_datasets/partitioner/linear_partitioner.py  | 2 +-
 .../flwr_datasets/partitioner/natural_id_partitioner.py   | 6 +++---
 .../partitioner/natural_id_partitioner_test.py            | 2 +-
 datasets/flwr_datasets/partitioner/partitioner.py         | 4 ++--
 datasets/flwr_datasets/partitioner/size_partitioner.py    | 8 ++++----
 datasets/flwr_datasets/partitioner/square_partitioner.py  | 2 +-
 10 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/datasets/README.md b/datasets/README.md
index 982da3760cc7..f4b3fab73d8e 100644
--- a/datasets/README.md
+++ b/datasets/README.md
@@ -53,7 +53,7 @@ If you plan to change the type of the dataset to run the code with your ML frame
 
 # Usage
 
-The Flower Datasets exposes `FederatedDataset(dataset, partitioners)` abstraction to represent the dataset needed for federated learning/analytics. It has two powerful methods that let you handle the dataset preprocessing. They are `load_partition(idx, split)` and `load_full(split)`.
+The Flower Datasets exposes `FederatedDataset(dataset, partitioners)` abstraction to represent the dataset needed for federated learning/analytics. It has two powerful methods that let you handle the dataset preprocessing. They are `load_partition(node_id, split)` and `load_full(split)`.
 
 Here's a quick example of how to partition the MNIST dataset:
 
diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py
index 52acc4d40100..cad137a98ba8 100644
--- a/datasets/flwr_datasets/federated_dataset.py
+++ b/datasets/flwr_datasets/federated_dataset.py
@@ -101,7 +101,7 @@ def __init__(
         # Indicate if the dataset is prepared for `load_partition` or `load_full`
         self._dataset_prepared: bool = False
 
-    def load_partition(self, idx: int, split: Optional[str] = None) -> Dataset:
+    def load_partition(self, node_id: int, split: Optional[str] = None) -> Dataset:
         """Load the partition specified by the idx in the selected split.
 
         The dataset is downloaded only when the first call to `load_partition` or
@@ -109,7 +109,7 @@ def load_partition(self, idx: int, split: Optional[str] = None) -> Dataset:
 
         Parameters
         ----------
-        idx : int
+        node_id : int
             Partition index for the selected split, idx in {0, ..., num_partitions - 1}.
         split : Optional[str]
             Name of the (partitioned) split (e.g. "train", "test"). You can skip this
@@ -135,7 +135,7 @@ def load_partition(self, idx: int, split: Optional[str] = None) -> Dataset:
         self._check_if_split_possible_to_federate(split)
         partitioner: Partitioner = self._partitioners[split]
         self._assign_dataset_to_partitioner(split)
-        return partitioner.load_partition(idx)
+        return partitioner.load_partition(node_id)
 
     def load_full(self, split: str) -> Dataset:
         """Load the full split of the dataset.
diff --git a/datasets/flwr_datasets/partitioner/exponential_partitioner.py b/datasets/flwr_datasets/partitioner/exponential_partitioner.py
index 07bb07cea9a5..10b11eb3e126 100644
--- a/datasets/flwr_datasets/partitioner/exponential_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/exponential_partitioner.py
@@ -21,7 +21,7 @@
 
 
 class ExponentialPartitioner(SizePartitioner):
-    """Partitioner creates partitions of size that are correlated with exp(idx).
+    """Partitioner creates partitions of size that are correlated with exp(node_id).
 
     The amount of data each client gets is correlated with the exponent of partition ID.
     For instance, if the IDs range from 1 to M, client with ID 1 gets e units of
@@ -29,7 +29,7 @@ class ExponentialPartitioner(SizePartitioner):
     The floor operation is applied on each of these numbers, it means floor(2.71...)
     = 2; e^2 ~ 7.39 floor(7.39) = 7. The number is rounded down = the fraction is
     always cut. The remainders of theses unassigned (fraction) samples is added to the
-    biggest partition (the one with the biggest idx).
+    biggest partition (the one with the biggest node_id).
 
     Parameters
     ----------
diff --git a/datasets/flwr_datasets/partitioner/iid_partitioner.py b/datasets/flwr_datasets/partitioner/iid_partitioner.py
index c8dbf8294fec..2df0d7df1ebf 100644
--- a/datasets/flwr_datasets/partitioner/iid_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/iid_partitioner.py
@@ -34,12 +34,12 @@ def __init__(self, num_partitions: int) -> None:
             raise ValueError("The number of partitions must be greater than zero.")
         self._num_partitions = num_partitions
 
-    def load_partition(self, idx: int) -> datasets.Dataset:
+    def load_partition(self, node_id: int) -> datasets.Dataset:
         """Load a single IID partition based on the partition index.
 
         Parameters
         ----------
-        idx: int
+        node_id : int
             the index that corresponds to the requested partition
 
         Returns
@@ -48,5 +48,5 @@ def load_partition(self, idx: int) -> datasets.Dataset:
             single dataset partition
         """
         return self.dataset.shard(
-            num_shards=self._num_partitions, index=idx, contiguous=True
+            num_shards=self._num_partitions, index=node_id, contiguous=True
         )
diff --git a/datasets/flwr_datasets/partitioner/linear_partitioner.py b/datasets/flwr_datasets/partitioner/linear_partitioner.py
index a9dc8b020c08..f77b0b87146d 100644
--- a/datasets/flwr_datasets/partitioner/linear_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/linear_partitioner.py
@@ -19,7 +19,7 @@
 
 
 class LinearPartitioner(SizePartitioner):
-    """Partitioner creates partitions of size that are linearly correlated with idx.
+    """Partitioner creates partitions of size that are linearly correlated with node_id.
 
     The amount of data each client gets is linearly correlated with the partition ID.
     For instance, if the IDs range from 1 to M, client with ID 1 gets 1 unit of data,
diff --git a/datasets/flwr_datasets/partitioner/natural_id_partitioner.py b/datasets/flwr_datasets/partitioner/natural_id_partitioner.py
index 9591cf911139..bc6c19cd9cb3 100644
--- a/datasets/flwr_datasets/partitioner/natural_id_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/natural_id_partitioner.py
@@ -42,7 +42,7 @@ def _create_int_node_id_to_natural_id(self) -> None:
             zip(range(len(unique_natural_ids)), unique_natural_ids)
         )
 
-    def load_partition(self, idx: int) -> datasets.Dataset:
+    def load_partition(self, node_id: int) -> datasets.Dataset:
         """Load a single partition corresponding to a single `node_id`.
 
         The choice of the partition is based on unique integers assigned to each
@@ -50,7 +50,7 @@ def load_partition(self, idx: int) -> datasets.Dataset:
 
         Parameters
         ----------
-        idx: int
+        node_id : int
             the index that corresponds to the requested partition
 
         Returns
@@ -62,7 +62,7 @@ def load_partition(self, idx: int) -> datasets.Dataset:
             self._create_int_node_id_to_natural_id()
 
         return self.dataset.filter(
-            lambda row: row[self._partition_by] == self._node_id_to_natural_id[idx]
+            lambda row: row[self._partition_by] == self._node_id_to_natural_id[node_id]
         )
 
     @property
diff --git a/datasets/flwr_datasets/partitioner/natural_id_partitioner_test.py b/datasets/flwr_datasets/partitioner/natural_id_partitioner_test.py
index 4e382b77095c..fb296294aec3 100644
--- a/datasets/flwr_datasets/partitioner/natural_id_partitioner_test.py
+++ b/datasets/flwr_datasets/partitioner/natural_id_partitioner_test.py
@@ -105,7 +105,7 @@ def test_correct_number_of_partitions(
     ) -> None:
         """Test if the # of available partitions is equal to # of unique clients."""
         _, partitioner = _dummy_setup(num_rows, num_unique_natural_ids)
-        _ = partitioner.load_partition(idx=0)
+        _ = partitioner.load_partition(node_id=0)
         self.assertEqual(len(partitioner.node_id_to_natural_id), num_unique_natural_ids)
 
     def test_cannot_set_node_id_to_natural_id(self) -> None:
diff --git a/datasets/flwr_datasets/partitioner/partitioner.py b/datasets/flwr_datasets/partitioner/partitioner.py
index f154d265175a..7ab15a54d49e 100644
--- a/datasets/flwr_datasets/partitioner/partitioner.py
+++ b/datasets/flwr_datasets/partitioner/partitioner.py
@@ -53,12 +53,12 @@ def dataset(self, value: Dataset) -> None:
         self._dataset = value
 
     @abstractmethod
-    def load_partition(self, idx: int) -> Dataset:
+    def load_partition(self, node_id: int) -> Dataset:
         """Load a single partition based on the partition index.
 
         Parameters
         ----------
-        idx: int
+        node_id: int
             the index that corresponds to the requested partition
 
         Returns
diff --git a/datasets/flwr_datasets/partitioner/size_partitioner.py b/datasets/flwr_datasets/partitioner/size_partitioner.py
index 27e1f528dc52..35ca750949ee 100644
--- a/datasets/flwr_datasets/partitioner/size_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/size_partitioner.py
@@ -64,14 +64,14 @@ def __init__(
         # A flag to perform only a single compute to determine the indices
         self._node_id_to_indices_determined = False
 
-    def load_partition(self, idx: int) -> datasets.Dataset:
+    def load_partition(self, node_id: int) -> datasets.Dataset:
         """Load a single partition based on the partition index.
 
-        For this partitioner the number of samples is dependent on the partition idx.
+        The number of samples is dependent on the partition node_id.
 
         Parameters
         ----------
-        idx : int
+        node_id : int
             the index that corresponds to the requested partition
 
         Returns
@@ -82,7 +82,7 @@ def load_partition(self, idx: int) -> datasets.Dataset:
         # The partitioning is done lazily - only when the first partition is requested.
         # A single run creates the indices assignments for all the partition indices.
         self._determine_node_id_to_indices_if_needed()
-        return self.dataset.select(self._node_id_to_indices[idx])
+        return self.dataset.select(self._node_id_to_indices[node_id])
 
     @property
     def node_id_to_size(self) -> Dict[int, int]:
diff --git a/datasets/flwr_datasets/partitioner/square_partitioner.py b/datasets/flwr_datasets/partitioner/square_partitioner.py
index 25affaf304ef..109b8397870b 100644
--- a/datasets/flwr_datasets/partitioner/square_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/square_partitioner.py
@@ -21,7 +21,7 @@
 
 
 class SquarePartitioner(SizePartitioner):
-    """Partitioner creates partitions of size that are correlated with squared idx.
+    """Partitioner creates partitions of size that are correlated with squared node_id.
 
     The amount of data each client gets is correlated with the squared partition ID.
     For instance, if the IDs range from 1 to M, client with ID 1 gets 1 unit of data,

From 60199e7a0dcb84462963eb029c41735dd9f0e84d Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 15:26:35 +0100
Subject: [PATCH 08/64] Clarify partitioners parameter description in docs
 (#2603)

---
 datasets/flwr_datasets/federated_dataset.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py
index cad137a98ba8..2f755a124afb 100644
--- a/datasets/flwr_datasets/federated_dataset.py
+++ b/datasets/flwr_datasets/federated_dataset.py
@@ -49,7 +49,8 @@ class FederatedDataset:
     partitioners : Dict[str, Union[Partitioner, int]]
         A dictionary mapping the Dataset split (a `str`) to a `Partitioner` or an `int`
         (representing the number of IID partitions that this split should be partitioned
-        into).
+        into). One or multiple `Partitioner`s can be specified in that manner, but at
+        most, one per split.
     shuffle : bool
         Whether to randomize the order of samples. Applied prior to resplitting,
         speratelly to each of the present splits in the dataset. It uses the `seed`

From 59b8c8c7be81b3fff95d64943ce267818a1131ff Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 15:51:36 +0100
Subject: [PATCH 09/64] Fix spacing around : in parameters in docs (#2601)

---
 datasets/flwr_datasets/partitioner/iid_partitioner.py       | 4 ++--
 .../flwr_datasets/partitioner/natural_id_partitioner.py     | 2 +-
 datasets/flwr_datasets/partitioner/partitioner.py           | 6 +++---
 datasets/flwr_datasets/resplitter/merge_resplitter.py       | 2 +-
 datasets/flwr_datasets/utils.py                             | 4 ++--
 5 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/datasets/flwr_datasets/partitioner/iid_partitioner.py b/datasets/flwr_datasets/partitioner/iid_partitioner.py
index 2df0d7df1ebf..c72b34f081f2 100644
--- a/datasets/flwr_datasets/partitioner/iid_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/iid_partitioner.py
@@ -24,7 +24,7 @@ class IidPartitioner(Partitioner):
 
     Parameters
     ----------
-    num_partitions: int
+    num_partitions : int
         The total number of partitions that the data will be divided into.
     """
 
@@ -44,7 +44,7 @@ def load_partition(self, node_id: int) -> datasets.Dataset:
 
         Returns
         -------
-        dataset_partition: Dataset
+        dataset_partition : Dataset
             single dataset partition
         """
         return self.dataset.shard(
diff --git a/datasets/flwr_datasets/partitioner/natural_id_partitioner.py b/datasets/flwr_datasets/partitioner/natural_id_partitioner.py
index bc6c19cd9cb3..b8f28696f3b7 100644
--- a/datasets/flwr_datasets/partitioner/natural_id_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/natural_id_partitioner.py
@@ -55,7 +55,7 @@ def load_partition(self, node_id: int) -> datasets.Dataset:
 
         Returns
         -------
-        dataset_partition: Dataset
+        dataset_partition : Dataset
             single dataset partition
         """
         if len(self._node_id_to_natural_id) == 0:
diff --git a/datasets/flwr_datasets/partitioner/partitioner.py b/datasets/flwr_datasets/partitioner/partitioner.py
index 7ab15a54d49e..92405152efc6 100644
--- a/datasets/flwr_datasets/partitioner/partitioner.py
+++ b/datasets/flwr_datasets/partitioner/partitioner.py
@@ -58,12 +58,12 @@ def load_partition(self, node_id: int) -> Dataset:
 
         Parameters
         ----------
-        node_id: int
+        node_id : int
             the index that corresponds to the requested partition
 
         Returns
         -------
-        dataset_partition: Dataset
+        dataset_partition : Dataset
             single dataset partition
         """
 
@@ -75,7 +75,7 @@ def is_dataset_assigned(self) -> bool:
 
         Returns
         -------
-        dataset_assigned: bool
+        dataset_assigned : bool
             True if a dataset is assigned, otherwise False.
         """
         return self._dataset is not None
diff --git a/datasets/flwr_datasets/resplitter/merge_resplitter.py b/datasets/flwr_datasets/resplitter/merge_resplitter.py
index 995b0e8e5602..6bb8f23e60dc 100644
--- a/datasets/flwr_datasets/resplitter/merge_resplitter.py
+++ b/datasets/flwr_datasets/resplitter/merge_resplitter.py
@@ -32,7 +32,7 @@ class MergeResplitter:
 
     Parameters
     ----------
-    merge_config: Dict[str, Tuple[str, ...]]
+    merge_config : Dict[str, Tuple[str, ...]]
         Dictionary with keys - the desired split names to values - tuples of the current
         split names that will be merged together
 
diff --git a/datasets/flwr_datasets/utils.py b/datasets/flwr_datasets/utils.py
index 49c65e9893a7..61fdc5b1db83 100644
--- a/datasets/flwr_datasets/utils.py
+++ b/datasets/flwr_datasets/utils.py
@@ -38,12 +38,12 @@ def _instantiate_partitioners(
 
     Parameters
     ----------
-    partitioners: Dict[str, Union[Partitioner, int]]
+    partitioners : Dict[str, Union[Partitioner, int]]
         Dataset split to the Partitioner or a number of IID partitions.
 
     Returns
     -------
-    partitioners: Dict[str, Partitioner]
+    partitioners : Dict[str, Partitioner]
         Partitioners specified as split to Partitioner object.
     """
     instantiated_partitioners: Dict[str, Partitioner] = {}

From d8fef6e3dfa8a5e92aebf7dbf740082a9c28c43d Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 16:42:17 +0100
Subject: [PATCH 10/64] Move the Resplitter type definition to resplitter
 package (#2604)

---
 datasets/flwr_datasets/common/__init__.py                    | 5 -----
 datasets/flwr_datasets/federated_dataset.py                  | 2 +-
 datasets/flwr_datasets/resplitter/__init__.py                | 2 ++
 .../{common/typing.py => resplitter/resplitter.py}           | 2 +-
 datasets/flwr_datasets/utils.py                              | 2 +-
 5 files changed, 5 insertions(+), 8 deletions(-)
 rename datasets/flwr_datasets/{common/typing.py => resplitter/resplitter.py} (95%)

diff --git a/datasets/flwr_datasets/common/__init__.py b/datasets/flwr_datasets/common/__init__.py
index a6468bcf7fda..b4f12f8641b3 100644
--- a/datasets/flwr_datasets/common/__init__.py
+++ b/datasets/flwr_datasets/common/__init__.py
@@ -13,8 +13,3 @@
 # limitations under the License.
 # ==============================================================================
 """Common components in Flower Datasets."""
-
-
-from .typing import Resplitter
-
-__all__ = ["Resplitter"]
diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py
index 2f755a124afb..d102df2c9ae6 100644
--- a/datasets/flwr_datasets/federated_dataset.py
+++ b/datasets/flwr_datasets/federated_dataset.py
@@ -19,8 +19,8 @@
 
 import datasets
 from datasets import Dataset, DatasetDict
-from flwr_datasets.common import Resplitter
 from flwr_datasets.partitioner import Partitioner
+from flwr_datasets.resplitter import Resplitter
 from flwr_datasets.utils import (
     _check_if_dataset_tested,
     _instantiate_partitioners,
diff --git a/datasets/flwr_datasets/resplitter/__init__.py b/datasets/flwr_datasets/resplitter/__init__.py
index f778d2096b76..e0b2dc0dcc1c 100644
--- a/datasets/flwr_datasets/resplitter/__init__.py
+++ b/datasets/flwr_datasets/resplitter/__init__.py
@@ -16,7 +16,9 @@
 
 
 from .merge_resplitter import MergeResplitter
+from .resplitter import Resplitter
 
 __all__ = [
     "MergeResplitter",
+    "Resplitter",
 ]
diff --git a/datasets/flwr_datasets/common/typing.py b/datasets/flwr_datasets/resplitter/resplitter.py
similarity index 95%
rename from datasets/flwr_datasets/common/typing.py
rename to datasets/flwr_datasets/resplitter/resplitter.py
index 28e6bae4a505..206e2e85730c 100644
--- a/datasets/flwr_datasets/common/typing.py
+++ b/datasets/flwr_datasets/resplitter/resplitter.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Flower Datasets type definitions."""
+"""Resplitter."""
 
 
 from typing import Callable
diff --git a/datasets/flwr_datasets/utils.py b/datasets/flwr_datasets/utils.py
index 61fdc5b1db83..e3d0fdfffa63 100644
--- a/datasets/flwr_datasets/utils.py
+++ b/datasets/flwr_datasets/utils.py
@@ -18,8 +18,8 @@
 import warnings
 from typing import Dict, Optional, Tuple, Union, cast
 
-from flwr_datasets.common import Resplitter
 from flwr_datasets.partitioner import IidPartitioner, Partitioner
+from flwr_datasets.resplitter import Resplitter
 from flwr_datasets.resplitter.merge_resplitter import MergeResplitter
 
 tested_datasets = [

From 0c3d8b0954117e8e6996f54489210f9e9d5ceced Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 16:49:35 +0100
Subject: [PATCH 11/64] Update FDS README (#2605)

---
 datasets/README.md | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/datasets/README.md b/datasets/README.md
index f4b3fab73d8e..876b6f453fa5 100644
--- a/datasets/README.md
+++ b/datasets/README.md
@@ -3,7 +3,7 @@
 [![GitHub license](https://img.shields.io/github/license/adap/flower)](https://github.com/adap/flower/blob/main/LICENSE)
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
 ![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)
-![Downloads](https://pepy.tech/badge/flwr)
+![Downloads](https://pepy.tech/badge/flwr-datasets)
 [![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.dev/join-slack)
 
 Flower Datasets (`flwr-datasets`) is a library to quickly and easily create datasets for federated learning, federated evaluation, and federated analytics. It was created by the `Flower Labs` team that also created Flower: A Friendly Federated Learning Framework. 
@@ -22,7 +22,13 @@ Thanks to using Hugging Face's `datasets` used under the hood, Flower Datasets i
 * Arrow.
 
 Create **custom partitioning schemes** or choose from the **implemented partitioning schemes**:
+* Partitioner (the abstract base class) `Partitioner`
 * IID partitioning `IidPartitioner(num_partitions)`
+* Natural ID partitioner `NaturalIdPartitioner`
+* Size partitioner (the abstract base class for the partitioners dictating the division based the number of samples) `SizePartitioner` 
+* Linear partitioner `LinearPartitioner`
+* Square partitioner `SquarePartitioner`
+* Exponential partitioner `ExponentialPartitioner`
 * more to come in future releases.
 
 # Installation
@@ -53,9 +59,9 @@ If you plan to change the type of the dataset to run the code with your ML frame
 
 # Usage
 
-The Flower Datasets exposes `FederatedDataset(dataset, partitioners)` abstraction to represent the dataset needed for federated learning/analytics. It has two powerful methods that let you handle the dataset preprocessing. They are `load_partition(node_id, split)` and `load_full(split)`.
+Flower Datasets exposes the `FederatedDataset` abstraction to represent the dataset needed for federated learning/evaluation/analytics. It has two powerful methods that let you handle the dataset preprocessing: `load_partition(node_id, split)` and `load_full(split)`.
 
-Here's a quick example of how to partition the MNIST dataset:
+Here's a basic quickstart example of how to partition the MNIST dataset:
 
 ```
 from flwr_datasets import FederatedDataset
@@ -68,23 +74,17 @@ mnist_partition_0 = mnist_fds.load_partition(0, "train")
 centralized_data = mnist_fds.load_full("test")
 ```
 
-`FederatedDataset(dataset, partitioners)` allows you specification of:
-
-* `dataset:str` - the name of the dataset.
-
-* `partitioners: Dict[str: int]` - `{split_name: str` to `number-of-partitions: int}` - partitioner that will be used with an associated split of the dataset e.g. `{"train": 100}`. It assumes by default the i.i.d. partitioning.
-
-More customization of `partitioners` is coming in future releases. 
+For more details, please refer to the specific how-to guides or tutorial. They showcase customization and more advanced features.
 
 # Future release
 
 Here are a few of the things that we will work on in future releases:
 
-* Support for more datasets (especially the ones that have user id present).
-* Creation of custom `Partitioner`s.
-* More out-of-the-box `Partitioner`s.
-* Passing `Partitioner`s via `FederatedDataset`'s `partitioner` argument. 
-* Customization of the dataset splitting before the partitioning.
+* ✅ Support for more datasets (especially the ones that have user id present).
+* ✅ Creation of custom `Partitioner`s.
+* ✅ More out-of-the-box `Partitioner`s.
+* ✅ Passing `Partitioner`s via `FederatedDataset`'s `partitioners` argument. 
+* ✅ Customization of the dataset splitting before the partitioning.
 * Simplification of the dataset transformation to the popular frameworks/types.
 * Creation of the synthetic data,
 * Support for Vertical FL.

From eead07f5b721f347ee0bc836e6d8515054b7ce6e Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 16:58:10 +0100
Subject: [PATCH 12/64] Bump up the FDS library version (#2606)

---
 datasets/pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml
index 62eaea3e5d48..7145345f7d94 100644
--- a/datasets/pyproject.toml
+++ b/datasets/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "flwr-datasets"
-version = "0.0.1"
+version = "0.0.2"
 description = "Flower Datasets"
 license = "Apache-2.0"
 authors = ["The Flower Authors <hello@flower.dev>"]

From 760a4564b075fdaed7d3ee8b06607629fe84edf9 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Wed, 15 Nov 2023 16:19:11 +0000
Subject: [PATCH 13/64] Fix incorrect docstring for 'certificates' in
 'start_driver' API (#2607)

---
 src/py/flwr/driver/app.py | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/src/py/flwr/driver/app.py b/src/py/flwr/driver/app.py
index 7c2c576c2c43..3cb8652365d8 100644
--- a/src/py/flwr/driver/app.py
+++ b/src/py/flwr/driver/app.py
@@ -19,7 +19,8 @@
 import threading
 import time
 from logging import INFO
-from typing import Dict, Optional
+from pathlib import Path
+from typing import Dict, Optional, Union
 
 from flwr.common import EventType, event
 from flwr.common.address import parse_address
@@ -51,7 +52,7 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     config: Optional[ServerConfig] = None,
     strategy: Optional[Strategy] = None,
     client_manager: Optional[ClientManager] = None,
-    certificates: Optional[bytes] = None,
+    root_certificates: Optional[Union[bytes, str]] = None,
 ) -> History:
     """Start a Flower Driver API server.
 
@@ -76,14 +77,10 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
         `flwr.driver.driver_client_manager.DriverClientManager`. If no
         implementation is provided, then `start_driver` will use
         `flwr.driver.driver_client_manager.DriverClientManager`.
-    certificates : bytes (default: None)
-        Tuple containing root certificate, server certificate, and private key
-        to start a secure SSL-enabled server. The tuple is expected to have
-        three bytes elements in the following order:
-
-            * CA certificate.
-            * server certificate.
-            * server private key.
+    root_certificates : Optional[Union[bytes, str]] (default: None)
+        The PEM-encoded root certificates as a byte string or a path string.
+        If provided, a secure connection using the certificates will be
+        established to an SSL-enabled Flower server.
 
     Returns
     -------
@@ -99,7 +96,7 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     Starting a driver that connects to an SSL-enabled server:
 
     >>> start_driver(
-    >>>     certificates=Path("/crts/root.pem").read_bytes()
+    >>>     root_certificates=Path("/crts/root.pem").read_bytes()
     >>> )
     """
     event(EventType.START_DRIVER_ENTER)
@@ -112,7 +109,9 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}"
 
     # Create the Driver
-    driver = GrpcDriver(driver_service_address=address, certificates=certificates)
+    if isinstance(root_certificates, str):
+        root_certificates = Path(root_certificates).read_bytes()
+    driver = GrpcDriver(driver_service_address=address, certificates=root_certificates)
     driver.connect()
     lock = threading.Lock()
 

From f0561759aafcbba2283ce308ef1eecad6de8dff2 Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Wed, 15 Nov 2023 18:42:18 +0000
Subject: [PATCH 14/64] Quickstart-xgboost with bagging aggregation (#2554)

Co-authored-by: yan-gao-GY <yan@flower.dev>
---
 examples/xgboost-comprehensive/README.md      |  87 +++++++++
 examples/xgboost-comprehensive/client.py      | 174 ++++++++++++++++++
 examples/xgboost-comprehensive/dataset.py     |  67 +++++++
 examples/xgboost-comprehensive/pyproject.toml |  15 ++
 .../xgboost-comprehensive/requirements.txt    |   3 +
 examples/xgboost-comprehensive/run.sh         |  17 ++
 examples/xgboost-comprehensive/server.py      | 111 +++++++++++
 examples/xgboost-comprehensive/strategy.py    | 139 ++++++++++++++
 examples/xgboost-comprehensive/utils.py       |  72 ++++++++
 9 files changed, 685 insertions(+)
 create mode 100644 examples/xgboost-comprehensive/README.md
 create mode 100644 examples/xgboost-comprehensive/client.py
 create mode 100644 examples/xgboost-comprehensive/dataset.py
 create mode 100644 examples/xgboost-comprehensive/pyproject.toml
 create mode 100644 examples/xgboost-comprehensive/requirements.txt
 create mode 100755 examples/xgboost-comprehensive/run.sh
 create mode 100644 examples/xgboost-comprehensive/server.py
 create mode 100644 examples/xgboost-comprehensive/strategy.py
 create mode 100644 examples/xgboost-comprehensive/utils.py

diff --git a/examples/xgboost-comprehensive/README.md b/examples/xgboost-comprehensive/README.md
new file mode 100644
index 000000000000..3801d4813a26
--- /dev/null
+++ b/examples/xgboost-comprehensive/README.md
@@ -0,0 +1,87 @@
+# Flower Example using XGBoost
+
+This example demonstrates how to perform EXtreme Gradient Boosting (XGBoost) within Flower using `xgboost` package.
+Tree-based with bagging method is used for aggregation on the server.
+
+## Project Setup
+
+Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you:
+
+```shell
+git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/quickstart-xgboost . && rm -rf flower && cd quickstart-xgboost
+```
+
+This will create a new directory called `quickstart-xgboost` containing the following files:
+
+```
+-- README.md         <- Your're reading this right now
+-- server.py         <- Defines the server-side logic
+-- strategy.py       <- Defines the tree-based bagging aggregation
+-- client.py         <- Defines the client-side logic
+-- dataset.py        <- Defines the functions of data loading and partitioning
+-- pyproject.toml    <- Example dependencies (if you use Poetry)
+-- requirements.txt  <- Example dependencies
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `xgboost` and `flwr`) are defined in `pyproject.toml` and `requirements.txt`. We recommend [Poetry](https://python-poetry.org/docs/) to install those dependencies and manage your virtual environment ([Poetry installation](https://python-poetry.org/docs/#installation)) or [pip](https://pip.pypa.io/en/latest/development/), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.
+
+#### Poetry
+
+```shell
+poetry install
+poetry shell
+```
+
+Poetry will install all your dependencies in a newly created virtual environment. To verify that everything works correctly you can run the following command:
+
+```shell
+poetry run python3 -c "import flwr"
+```
+
+If you don't see any errors you're good to go!
+
+#### pip
+
+Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt.
+
+```shell
+pip install -r requirements.txt
+```
+
+## Run Federated Learning with XGBoost and Flower
+
+Afterwards you are ready to start the Flower server as well as the clients.
+You can simply start the server in a terminal as follows:
+
+```shell
+python3 server.py
+```
+
+Now you are ready to start the Flower clients which will participate in the learning.
+To do so simply open two more terminal windows and run the following commands.
+
+Start client 1 in the first terminal:
+
+```shell
+python3 client.py --node-id=0
+```
+
+Start client 2 in the second terminal:
+
+```shell
+python3 client.py --node-id=1
+```
+
+You will see that XGBoost is starting a federated training.
+
+Alternatively, you can use `run.sh` to run the same experiment in a single terminal as follows:
+
+```shell
+bash run.sh
+```
+
+Besides, we provide options to customise the experimental settings, including data partitioning and centralised/distributed evaluation (see `utils.py`).
+Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-xgboost)
+and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
diff --git a/examples/xgboost-comprehensive/client.py b/examples/xgboost-comprehensive/client.py
new file mode 100644
index 000000000000..5aba30266b5a
--- /dev/null
+++ b/examples/xgboost-comprehensive/client.py
@@ -0,0 +1,174 @@
+import warnings
+from logging import INFO
+import xgboost as xgb
+
+import flwr as fl
+from flwr_datasets import FederatedDataset
+from flwr.common.logger import log
+from flwr.common import (
+    Code,
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    GetParametersIns,
+    GetParametersRes,
+    Parameters,
+    Status,
+)
+
+from dataset import (
+    instantiate_partitioner,
+    train_test_split,
+    transform_dataset_to_dmatrix,
+    resplit,
+)
+from utils import client_args_parser
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+# Parse arguments for experimental settings
+args = client_args_parser()
+
+# Load (HIGGS) dataset and conduct partitioning
+num_partitions = args.num_partitions
+
+# Partitioner type is chosen from ["uniform", "linear", "square", "exponential"]
+partitioner_type = args.partitioner_type
+
+# Instantiate partitioner
+partitioner = instantiate_partitioner(
+    partitioner_type=partitioner_type, num_partitions=num_partitions
+)
+fds = FederatedDataset(
+    dataset="jxie/higgs", partitioners={"train": partitioner}, resplitter=resplit
+)
+
+# Let's use the first partition as an example
+node_id = args.node_id
+partition = fds.load_partition(idx=node_id, split="train")
+partition.set_format("numpy")
+
+if args.centralised_eval:
+    # Use centralised test set for evaluation
+    train_data = partition
+    valid_data = fds.load_full("test")
+    valid_data.set_format("numpy")
+    num_train = train_data.shape[0]
+    num_val = valid_data.shape[0]
+else:
+    # Train/test splitting
+    SEED = args.seed
+    test_fraction = args.test_fraction
+    train_data, valid_data, num_train, num_val = train_test_split(
+        partition, test_fraction=test_fraction, seed=SEED
+    )
+
+# Reformat data to DMatrix for xgboost
+train_dmatrix = transform_dataset_to_dmatrix(train_data)
+valid_dmatrix = transform_dataset_to_dmatrix(valid_data)
+
+
+# Hyper-parameters for xgboost training
+num_local_round = 1
+params = {
+    "objective": "binary:logistic",
+    "eta": 0.1,  # Learning rate
+    "max_depth": 8,
+    "eval_metric": "auc",
+    "nthread": 16,
+    "num_parallel_tree": 1,
+    "subsample": 1,
+    "tree_method": "hist",
+}
+
+
+# Define Flower client
+class FlowerClient(fl.client.Client):
+    def __init__(self):
+        self.bst = None
+        self.config = None
+
+    def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
+        _ = (self, ins)
+        return GetParametersRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[]),
+        )
+
+    def _local_boost(self):
+        # Update trees based on local training data.
+        for i in range(num_local_round):
+            self.bst.update(train_dmatrix, self.bst.num_boosted_rounds())
+
+        # Extract the last N=num_local_round trees for sever aggregation
+        bst = self.bst[
+            self.bst.num_boosted_rounds()
+            - num_local_round : self.bst.num_boosted_rounds()
+        ]
+
+        return bst
+
+    def fit(self, ins: FitIns) -> FitRes:
+        if not self.bst:
+            # First round local training
+            log(INFO, "Start training at round 1")
+            bst = xgb.train(
+                params,
+                train_dmatrix,
+                num_boost_round=num_local_round,
+                evals=[(valid_dmatrix, "validate"), (train_dmatrix, "train")],
+            )
+            self.config = bst.save_config()
+            self.bst = bst
+        else:
+            for item in ins.parameters.tensors:
+                global_model = bytearray(item)
+
+            # Load global model into booster
+            self.bst.load_model(global_model)
+            self.bst.load_config(self.config)
+
+            bst = self._local_boost()
+
+        local_model = bst.save_raw("json")
+        local_model_bytes = bytes(local_model)
+
+        return FitRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[local_model_bytes]),
+            num_examples=num_train,
+            metrics={},
+        )
+
+    def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
+        eval_results = self.bst.eval_set(
+            evals=[(valid_dmatrix, "valid")],
+            iteration=self.bst.num_boosted_rounds() - 1,
+        )
+        auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4)
+
+        global_round = ins.config["global_round"]
+        log(INFO, f"AUC = {auc} at round {global_round}")
+
+        return EvaluateRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            loss=0.0,
+            num_examples=num_val,
+            metrics={"AUC": auc},
+        )
+
+
+# Start Flower client
+fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient())
diff --git a/examples/xgboost-comprehensive/dataset.py b/examples/xgboost-comprehensive/dataset.py
new file mode 100644
index 000000000000..80c978f1077b
--- /dev/null
+++ b/examples/xgboost-comprehensive/dataset.py
@@ -0,0 +1,67 @@
+import xgboost as xgb
+from typing import Callable, Dict, List, Optional, Tuple, Union
+from datasets import Dataset, DatasetDict, concatenate_datasets
+from flwr_datasets.partitioner import (
+    IidPartitioner,
+    LinearPartitioner,
+    SquarePartitioner,
+    ExponentialPartitioner,
+)
+
+CORRELATION_TO_PARTITIONER = {
+    "uniform": IidPartitioner,
+    "linear": LinearPartitioner,
+    "square": SquarePartitioner,
+    "exponential": ExponentialPartitioner,
+}
+
+
+def instantiate_partitioner(partitioner_type: str, num_partitions: int):
+    """Initialise partitioner based on selected partitioner type and number of
+    partitions."""
+    partitioner = CORRELATION_TO_PARTITIONER[partitioner_type](
+        num_partitions=num_partitions
+    )
+    return partitioner
+
+
+def train_test_split(partition: Dataset, test_fraction: float, seed: int):
+    """Split the data into train and validation set given split rate."""
+    train_test = partition.train_test_split(test_size=test_fraction, seed=seed)
+    partition_train = train_test["train"]
+    partition_test = train_test["test"]
+
+    num_train = len(partition_train)
+    num_test = len(partition_test)
+
+    return partition_train, partition_test, num_train, num_test
+
+
+def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.DMatrix:
+    """Transform dataset to DMatrix format for xgboost."""
+    x = data["inputs"]
+    y = data["label"]
+    new_data = xgb.DMatrix(x, label=y)
+    return new_data
+
+
+def resplit(dataset: DatasetDict) -> DatasetDict:
+    """Increase the quantity of centralised test samples from 500K to 1M."""
+    return DatasetDict(
+        {
+            "train": dataset["train"].select(
+                range(0, dataset["train"].num_rows - 500_000)
+            ),
+            "test": concatenate_datasets(
+                [
+                    dataset["train"].select(
+                        range(
+                            dataset["train"].num_rows - 500_000,
+                            dataset["train"].num_rows,
+                        )
+                    ),
+                    dataset["test"],
+                ]
+            ),
+        }
+    )
diff --git a/examples/xgboost-comprehensive/pyproject.toml b/examples/xgboost-comprehensive/pyproject.toml
new file mode 100644
index 000000000000..5414b5122154
--- /dev/null
+++ b/examples/xgboost-comprehensive/pyproject.toml
@@ -0,0 +1,15 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "xgboost-comprehensive"
+version = "0.1.0"
+description = "Federated XGBoost with Flower (comprehensive)"
+authors = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+flwr-datasets = ">=0.0.2,<1.0.0"
+xgboost = ">=2.0.0,<3.0.0"
diff --git a/examples/xgboost-comprehensive/requirements.txt b/examples/xgboost-comprehensive/requirements.txt
new file mode 100644
index 000000000000..c6b9c1a67894
--- /dev/null
+++ b/examples/xgboost-comprehensive/requirements.txt
@@ -0,0 +1,3 @@
+flwr>=1.0, <2.0
+flwr-datasets>=0.0.2, <1.0.0
+xgboost>=2.0.0, <3.0.0
diff --git a/examples/xgboost-comprehensive/run.sh b/examples/xgboost-comprehensive/run.sh
new file mode 100755
index 000000000000..7cf65fa4d52d
--- /dev/null
+++ b/examples/xgboost-comprehensive/run.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
+
+echo "Starting server"
+python server.py &
+sleep 15  # Sleep for 15s to give the server enough time to start
+
+for i in `seq 0 1`; do
+    echo "Starting client $i"
+    python3 client.py --node-id=$i &
+done
+
+# Enable CTRL+C to stop all background processes
+trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+# Wait for all background processes to complete
+wait
diff --git a/examples/xgboost-comprehensive/server.py b/examples/xgboost-comprehensive/server.py
new file mode 100644
index 000000000000..857e99528013
--- /dev/null
+++ b/examples/xgboost-comprehensive/server.py
@@ -0,0 +1,111 @@
+from typing import Dict
+from logging import INFO
+import xgboost as xgb
+
+import flwr as fl
+from flwr.common.logger import log
+from flwr.common import Parameters, Scalar
+from flwr_datasets import FederatedDataset
+
+from strategy import FedXgbBagging
+from utils import server_args_parser
+from dataset import resplit, transform_dataset_to_dmatrix
+
+
+# Parse arguments for experimental settings
+args = server_args_parser()
+pool_size = args.pool_size
+num_rounds = args.num_rounds
+num_clients_per_round = args.num_clients_per_round
+num_evaluate_clients = args.num_evaluate_clients
+centralised_eval = args.centralised_eval
+
+# Load centralised test set
+if centralised_eval:
+    fds = FederatedDataset(
+        dataset="jxie/higgs", partitioners={"train": 20}, resplitter=resplit
+    )
+    test_set = fds.load_full("test")
+    test_set.set_format("numpy")
+    test_dmatrix = transform_dataset_to_dmatrix(test_set)
+
+# Hyper-parameters used for initialisation
+params = {
+    "objective": "binary:logistic",
+    "eta": 0.1,  # Learning rate
+    "max_depth": 8,
+    "eval_metric": "auc",
+    "nthread": 16,
+    "num_parallel_tree": 1,
+    "subsample": 1,
+    "tree_method": "hist",
+}
+
+
+def eval_config(rnd: int) -> Dict[str, str]:
+    """Return a configuration with global epochs."""
+    config = {
+        "global_round": str(rnd),
+    }
+    return config
+
+
+def evaluate_metrics_aggregation(eval_metrics):
+    """Return an aggregated metric (AUC) for evaluation."""
+    total_num = sum([num for num, _ in eval_metrics])
+    auc_aggregated = (
+        sum([metrics["AUC"] * num for num, metrics in eval_metrics]) / total_num
+    )
+    metrics_aggregated = {"AUC": auc_aggregated}
+    return metrics_aggregated
+
+
+def get_evaluate_fn(test_data):
+    """Return a function for centralised evaluation."""
+
+    def evaluate_fn(
+        server_round: int, parameters: Parameters, config: Dict[str, Scalar]
+    ):
+        # If at the first round, skip the evaluation
+        if server_round == 0:
+            return 0, {}
+        else:
+            bst = xgb.Booster(params=params)
+            for para in parameters.tensors:
+                para_b = bytearray(para)
+
+            # Load global model
+            bst.load_model(para_b)
+            # Run evaluation
+            eval_results = bst.eval_set(
+                evals=[(test_data, "valid")],
+                iteration=bst.num_boosted_rounds() - 1,
+            )
+            auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4)
+            log(INFO, f"AUC = {auc} at round {server_round}")
+
+            return 0, {"AUC": auc}
+
+    return evaluate_fn
+
+
+# Define strategy
+strategy = FedXgbBagging(
+    evaluate_function=get_evaluate_fn(test_dmatrix) if centralised_eval else None,
+    fraction_fit=(float(num_clients_per_round) / pool_size),
+    min_fit_clients=num_clients_per_round,
+    min_available_clients=pool_size,
+    min_evaluate_clients=num_evaluate_clients if not centralised_eval else 0,
+    fraction_evaluate=1.0 if not centralised_eval else 0.0,
+    on_evaluate_config_fn=eval_config,
+    evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation
+    if not centralised_eval
+    else None,
+)
+
+# Start Flower server
+fl.server.start_server(
+    server_address="0.0.0.0:8080",
+    config=fl.server.ServerConfig(num_rounds=num_rounds),
+    strategy=strategy,
+)
diff --git a/examples/xgboost-comprehensive/strategy.py b/examples/xgboost-comprehensive/strategy.py
new file mode 100644
index 000000000000..814010720a77
--- /dev/null
+++ b/examples/xgboost-comprehensive/strategy.py
@@ -0,0 +1,139 @@
+from logging import WARNING
+from typing import Callable, Dict, List, Optional, Tuple, Union
+import flwr as fl
+import json
+
+from flwr.common import (
+    EvaluateRes,
+    FitRes,
+    Parameters,
+    Scalar,
+)
+from flwr.server.client_proxy import ClientProxy
+from flwr.common.logger import log
+
+
+class FedXgbBagging(fl.server.strategy.FedAvg):
+    def __init__(
+        self,
+        evaluate_function: Optional[
+            Callable[
+                [int, Parameters, Dict[str, Scalar]],
+                Optional[Tuple[float, Dict[str, Scalar]]],
+            ]
+        ] = None,
+        **kwargs,
+    ):
+        self.evaluate_function = evaluate_function
+        self.global_model = None
+        super().__init__(**kwargs)
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate fit results using bagging."""
+        if not results:
+            return None, {}
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Aggregate all the client trees
+        for _, fit_res in results:
+            update = fit_res.parameters.tensors
+            for item in update:
+                self.global_model = aggregate(
+                    self.global_model, json.loads(bytearray(item))
+                )
+
+        weights_avg = json.dumps(self.global_model)
+
+        return (
+            Parameters(tensor_type="", tensors=[bytes(weights_avg, "utf-8")]),
+            {},
+        )
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation metrics using average."""
+        if not results:
+            return None, {}
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Aggregate custom metrics if aggregation fn was provided
+        metrics_aggregated = {}
+        if self.evaluate_metrics_aggregation_fn:
+            eval_metrics = [(res.num_examples, res.metrics) for _, res in results]
+            metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics)
+        elif server_round == 1:  # Only log this warning once
+            log(WARNING, "No evaluate_metrics_aggregation_fn provided")
+
+        return 0, metrics_aggregated
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function."""
+        if self.evaluate_function is None:
+            # No evaluation function provided
+            return None
+        eval_res = self.evaluate_function(server_round, parameters, {})
+        if eval_res is None:
+            return None
+        loss, metrics = eval_res
+        return loss, metrics
+
+
+def aggregate(bst_prev: Optional[Dict], bst_curr: Dict) -> Dict:
+    """Conduct bagging aggregation for given trees."""
+    if not bst_prev:
+        return bst_curr
+    else:
+        # Get the tree numbers
+        tree_num_prev, paral_tree_num_prev = _get_tree_nums(bst_prev)
+        tree_num_curr, paral_tree_num_curr = _get_tree_nums(bst_curr)
+
+        bst_prev["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+            "num_trees"
+        ] = str(tree_num_prev + paral_tree_num_curr)
+        iteration_indptr = bst_prev["learner"]["gradient_booster"]["model"][
+            "iteration_indptr"
+        ]
+        bst_prev["learner"]["gradient_booster"]["model"]["iteration_indptr"].append(
+            iteration_indptr[-1] + paral_tree_num_curr
+        )
+
+        # Aggregate new trees
+        trees_curr = bst_curr["learner"]["gradient_booster"]["model"]["trees"]
+        for tree_count in range(paral_tree_num_curr):
+            trees_curr[tree_count]["id"] = tree_num_prev + tree_count
+            bst_prev["learner"]["gradient_booster"]["model"]["trees"].append(
+                trees_curr[tree_count]
+            )
+            bst_prev["learner"]["gradient_booster"]["model"]["tree_info"].append(0)
+        return bst_prev
+
+
+def _get_tree_nums(xgb_model: Dict) -> (int, int):
+    # Get the number of trees
+    tree_num = int(
+        xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+            "num_trees"
+        ]
+    )
+    # Get the number of parallel trees
+    paral_tree_num = int(
+        xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+            "num_parallel_tree"
+        ]
+    )
+    return tree_num, paral_tree_num
diff --git a/examples/xgboost-comprehensive/utils.py b/examples/xgboost-comprehensive/utils.py
new file mode 100644
index 000000000000..51c1a1b9604d
--- /dev/null
+++ b/examples/xgboost-comprehensive/utils.py
@@ -0,0 +1,72 @@
+import argparse
+
+
+def client_args_parser():
+    """Parse arguments to define experimental settings on client side."""
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--num-partitions", default=10, type=int, help="Number of partitions."
+    )
+    parser.add_argument(
+        "--partitioner-type",
+        default="uniform",
+        type=str,
+        choices=["uniform", "linear", "square", "exponential"],
+        help="Partitioner types.",
+    )
+    parser.add_argument(
+        "--node-id",
+        default=0,
+        type=int,
+        help="Node ID used for the current client.",
+    )
+    parser.add_argument(
+        "--seed", default=42, type=int, help="Seed used for train/test splitting."
+    )
+    parser.add_argument(
+        "--test-fraction",
+        default=0.2,
+        type=float,
+        help="Test fraction for train/test splitting.",
+    )
+    parser.add_argument(
+        "--centralised-eval",
+        action="store_true",
+        help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def server_args_parser():
+    """Parse arguments to define experimental settings on server side."""
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--pool-size", default=2, type=int, help="Number of total clients."
+    )
+    parser.add_argument(
+        "--num-rounds", default=5, type=int, help="Number of FL rounds."
+    )
+    parser.add_argument(
+        "--num-clients-per-round",
+        default=2,
+        type=int,
+        help="Number of clients participate in training each round.",
+    )
+    parser.add_argument(
+        "--num-evaluate-clients",
+        default=2,
+        type=int,
+        help="Number of clients selected for evaluation.",
+    )
+    parser.add_argument(
+        "--centralised-eval",
+        action="store_true",
+        help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
+    )
+
+    args = parser.parse_args()
+    return args

From ea8ac9e982648f90a28edd762c744ab395112374 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Thu, 16 Nov 2023 09:34:58 +0100
Subject: [PATCH 15/64] Remove existing dockerfiles (#2600)

Co-authored-by: Taner Topal <taner@flower.dev>
---
 src/docker/README.md          |  6 ------
 src/docker/build.sh           | 26 --------------------------
 src/docker/default.Dockerfile |  8 --------
 src/docker/ssh_key            | 27 ---------------------------
 src/docker/ssh_key.pub        |  1 -
 src/docker/sshd.Dockerfile    | 32 --------------------------------
 6 files changed, 100 deletions(-)
 delete mode 100644 src/docker/README.md
 delete mode 100755 src/docker/build.sh
 delete mode 100644 src/docker/default.Dockerfile
 delete mode 100644 src/docker/ssh_key
 delete mode 100644 src/docker/ssh_key.pub
 delete mode 100644 src/docker/sshd.Dockerfile

diff --git a/src/docker/README.md b/src/docker/README.md
deleted file mode 100644
index 0cebf9a6d737..000000000000
--- a/src/docker/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-# Docker
-
-We provide two docker images. They can be built using the build.sh script in the ./src/docker directory.
-The default image will contain the installed flower library with all dependencies to enable easily
-trying out flower. The second image is used in tests as well as local runs of baselines using the
-FlowerOps package.
diff --git a/src/docker/build.sh b/src/docker/build.sh
deleted file mode 100755
index ea6b643c1fb2..000000000000
--- a/src/docker/build.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../../
-
-HASH=$(printf "$(git rev-parse HEAD)\n$(git diff | sha1sum)" | sha1sum | cut -c1-7)
-
-rm -rf dist
-python -m poetry build
-docker build -f src/docker/default.Dockerfile -t flower:latest -t flower:$HASH .
-docker build -f src/docker/sshd.Dockerfile --build-arg SSH_PUBLIC_KEY="$(cat docker/ssh_key.pub)" -t flower-sshd:latest -t flower-sshd:$HASH .
diff --git a/src/docker/default.Dockerfile b/src/docker/default.Dockerfile
deleted file mode 100644
index 0ebd803fbd9c..000000000000
--- a/src/docker/default.Dockerfile
+++ /dev/null
@@ -1,8 +0,0 @@
-FROM python:3.7.15-slim-buster
-
-# Install the biggest dependencies before copying the wheel
-RUN pip install tensorflow-cpu==2.6.2 numpy==1.19.5
-
-COPY dist/flwr-1.6.0-py3-none-any.whl flwr-1.6.0-py3-none-any.whl
-RUN python3.7 -m pip install --no-cache-dir 'flwr-1.6.0-py3-none-any.whl[examples-pytorch,examples-tensorflow,http-logger,baseline,ops]'
-RUN rm flwr-1.6.0-py3-none-any.whl
diff --git a/src/docker/ssh_key b/src/docker/ssh_key
deleted file mode 100644
index c6fd85ac4bf5..000000000000
--- a/src/docker/ssh_key
+++ /dev/null
@@ -1,27 +0,0 @@
------BEGIN RSA PRIVATE KEY-----
-MIIEpAIBAAKCAQEAvp7rW7/QCAlXeM/U1QESOKD6aBJkLYLyjzD0A/E+rDz5FBV0
-O9gVhIzIwHYznEV9uWbwJfh5GhE5pb2Om2keERRy56/xoxc2LhMvt4fKf+CKoKYU
-fl7QYOHmmT6j4vcZX4Ao0ntHBgX6wk2z2F3Ma1lKmRgh8jHPFVRNHsTLV0WArfWS
-zegHsGeRN5aLwWjYZ3RIVzCNXsvDozyf88VUn75E2kwBhtg5OH0y5Rvkvm7oqzxt
-u4sNLEdcpIhdTSx8lOGqqaBBP4JcFIA1u8dDqSb3qnhOwW6D2rGloKKMkWCYI0RC
-IyXOTFWca/rQKBP9gkd4FSxTMNByF5DSpWm4awIDAQABAoIBAGxK/IrzPcxTAk6w
-Q8l/AksJqTjWufLhIcwXk4lp1KElQpwWVCJrcE4NbT9p54gwMmzR/jyUHLiicI2o
-UwwXe+1YIK9fVXfCCBXE8Kbjjjd36Um7EIl6dAeWTttgqtqWZuPKe2aYgpD5dKxO
-WI3jO+8Am1C+VQqzIWKMsgN46cx5xMmJs5WzhqKz//xO2Wrrs2MyPw0QZsHpm1j7
-RYLoPwl6EHN6Y1jWv55s0r3VIAGp4qjuR5BJaQu2ITeWU//TE1ouejMCEO8eLbIH
-aI1Ydod9BGxo0ET31GLuV+KM8ilk7+lfP8A2z4MryUEtBgZ+bukxZ6RTOtbXIsuR
-ccMAFoECgYEA4GJONbjrrBpEnn0wMKBRFYPpZ1chDC9X0qC0yUfatuiB4PzSsGEQ
-phJzCEX+fRG8giFTtWBzavBFdvto5JmYHnAoZ08oZqfI75ID5FmxmBHDbhKisUiF
-BCSo2MhWcQu/T7R26WaBDjWqal/xNEdIr5Gw3TP9reIU15xwLfe7eP8CgYEA2Xq/
-6pAKkxmjiOTDRwosyQ6g+d4uLAT8RClt/vLj5FUKtL3TXuCDFrA9iNROmHlAuj05
-PwV+3yV13y+BvkweaX5fIeY10axpr7nlozM2UUVnotTM8mrigPUNkP3jc+3DbtbF
-JJgvJgB6e/8fc7L/hf5yoALFeqnPVuoZ82rHtJUCgYEAqTobCDNeMt9gzzNVa+cN
-IC/rkU/KEdmG38jqcD+r+XTjt16WPAYIu2eAsEWxyHve8znVWx7SDV4G1dSI6Jfa
-fWJTZo7xxnJWfjBelRL8eNc00EhnwVMTVuubGFwHrZyRfbh6Uk9HmALrcuzHComU
-jS1Q+ZOjiEfRNke650BGeNsCgYBvKfJNWIE/PZy8zYoPluDbiJG6Btp8/7Net/qU
-ldztCpclWd5gIcx/3Wgmt7bQ8zGlwMOxNgLq1wAQK/1aoxfsfn7sGfsqiMGg5BnA
-ON4r3pkhvzpfM0lB94COu2f3CvKZhkhQ80UuXJI/LWILRtSbzjsTFTeSApiL6a+I
-H+2inQKBgQC0oTcFDUkuS4FRZb/SDfWIDm6RUVuRaSNJuX4aGlGAWFvtQxG+7Avz
-VroKqjMvtI5h8VutE8qELRQOdC0TfaUjjhWZ7oX4TxSVqzAtHnXt1PmvS0ih19cY
-ckpB+gFPXpgR1GwBi8GEcsWX2aNrYhCMvqD38bI2W8f23xciaSm4wg==
------END RSA PRIVATE KEY-----
diff --git a/src/docker/ssh_key.pub b/src/docker/ssh_key.pub
deleted file mode 100644
index 302b15494f93..000000000000
--- a/src/docker/ssh_key.pub
+++ /dev/null
@@ -1 +0,0 @@
-ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQC+nutbv9AICVd4z9TVARI4oPpoEmQtgvKPMPQD8T6sPPkUFXQ72BWEjMjAdjOcRX25ZvAl+HkaETmlvY6baR4RFHLnr/GjFzYuEy+3h8p/4IqgphR+XtBg4eaZPqPi9xlfgCjSe0cGBfrCTbPYXcxrWUqZGCHyMc8VVE0exMtXRYCt9ZLN6AewZ5E3lovBaNhndEhXMI1ey8OjPJ/zxVSfvkTaTAGG2Dk4fTLlG+S+buirPG27iw0sR1ykiF1NLHyU4aqpoEE/glwUgDW7x0OpJveqeE7BboPasaWgooyRYJgjREIjJc5MVZxr+tAoE/2CR3gVLFMw0HIXkNKlabhr tanto@R2U2
diff --git a/src/docker/sshd.Dockerfile b/src/docker/sshd.Dockerfile
deleted file mode 100644
index fc310dc5e1ce..000000000000
--- a/src/docker/sshd.Dockerfile
+++ /dev/null
@@ -1,32 +0,0 @@
-FROM python:3.7.15-slim-buster
-
-RUN apt-get update
-RUN apt-get install -y openssh-server screen
-RUN mkdir /var/run/sshd
-
-RUN echo 'root:root' | chpasswd
-
-RUN sed -ri 's/^#?PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config
-RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config
-
-RUN mkdir /root/.ssh
-
-ARG SSH_PUBLIC_KEY
-RUN echo $SSH_PUBLIC_KEY > /root/.ssh/authorized_keys
-
-RUN apt-get clean && \
-    rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-WORKDIR /root
-
-RUN python3.7 -m pip install tensorflow-cpu==2.6.0 torch==1.7.1 torchvision==0.8.2 numpy==1.19.5
-COPY dist/flwr-1.6.0-py3-none-any.whl flwr-1.6.0-py3-none-any.whl
-RUN python3.7 -m pip install --no-cache-dir 'flwr-1.6.0-py3-none-any.whl[examples-pytorch,examples-tensorflow,http-logger,baseline,ops]' && \
-    rm flwr-1.6.0-py3-none-any.whl
-
-RUN python3.7 -m flwr_experimental.baseline.tf_fashion_mnist.download
-RUN python3.7 -m flwr_experimental.baseline.tf_cifar.download
-
-EXPOSE 22
-
-CMD ["/usr/sbin/sshd", "-D"]

From 7ae14a28c1b7e6751da45aa4a194f8e30ccad6f1 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Thu, 16 Nov 2023 09:47:23 +0100
Subject: [PATCH 16/64] Add server dockerfile (#2602)

Co-authored-by: Taner Topal <taner@flower.dev>
---
 src/docker/server/Dockerfile | 49 ++++++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 src/docker/server/Dockerfile

diff --git a/src/docker/server/Dockerfile b/src/docker/server/Dockerfile
new file mode 100644
index 000000000000..d294013faefa
--- /dev/null
+++ b/src/docker/server/Dockerfile
@@ -0,0 +1,49 @@
+# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
+
+FROM ubuntu:22.04 as base
+
+ENV DEBIAN_FRONTEND noninteractive
+
+# Install system dependencies
+RUN apt-get update \
+    && apt-get -y --no-install-recommends install \
+    clang-format git unzip ca-certificates openssh-client liblzma-dev \
+    build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev wget\
+    libsqlite3-dev curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev \
+    libxmlsec1-dev libffi-dev liblzma-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install PyEnv and Python
+ARG PYTHON_VERSION
+ENV PYENV_ROOT /root/.pyenv
+ENV PATH $PYENV_ROOT/shims:$PYENV_ROOT/bin:$PATH
+RUN curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash
+RUN pyenv install ${PYTHON_VERSION} \
+    && pyenv global ${PYTHON_VERSION} \
+    && pyenv rehash
+
+# Install specific version of pip
+ARG PIP_VERSION
+RUN python -m pip install pip==$PIP_VERSION
+
+# Install specific version of setuptools
+ARG SETUPTOOLS_VERSION
+RUN python -m pip install setuptools==$SETUPTOOLS_VERSION
+
+# Install poetry as all examples use it and therefore it should be available for custom images
+ARG POETRY_VERSION
+RUN curl -sSL https://install.python-poetry.org | python3 - --version ${POETRY_VERSION}
+ENV PATH /root/.local/bin:$PATH
+RUN poetry config virtualenvs.create false
+
+# Server image
+FROM base as server
+
+WORKDIR /app
+ARG FLWR_VERSION
+RUN python -m pip install -U flwr[rest]==${FLWR_VERSION}
+ENTRYPOINT ["python", "-c", "from flwr.server import run_server\nrun_server()"]
+
+# Test if Flower can be successfully installed and imported
+FROM server as test
+RUN python -c "from flwr.server import run_server"

From 9ad0639b8ea7e7968bc02625539dcbe40db242d0 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 16 Nov 2023 13:55:25 +0100
Subject: [PATCH 17/64] FDS small docs fix (#2610)

---
 datasets/doc/source/conf.py                 | 2 +-
 datasets/flwr_datasets/federated_dataset.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/datasets/doc/source/conf.py b/datasets/doc/source/conf.py
index dad5f9f036a1..4fccaf0ef084 100644
--- a/datasets/doc/source/conf.py
+++ b/datasets/doc/source/conf.py
@@ -37,7 +37,7 @@
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
-release = "0.0.1"
+release = "0.0.2"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py
index d102df2c9ae6..c40f8cc34857 100644
--- a/datasets/flwr_datasets/federated_dataset.py
+++ b/datasets/flwr_datasets/federated_dataset.py
@@ -49,8 +49,8 @@ class FederatedDataset:
     partitioners : Dict[str, Union[Partitioner, int]]
         A dictionary mapping the Dataset split (a `str`) to a `Partitioner` or an `int`
         (representing the number of IID partitions that this split should be partitioned
-        into). One or multiple `Partitioner`s can be specified in that manner, but at
-        most, one per split.
+        into). One or multiple `Partitioner` objects can be specified in that manner,
+        but at most, one per split.
     shuffle : bool
         Whether to randomize the order of samples. Applied prior to resplitting,
         speratelly to each of the present splits in the dataset. It uses the `seed`

From f84f0781839c2ed8ce504f20c3eb00e6690b8953 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Thu, 16 Nov 2023 13:24:25 +0000
Subject: [PATCH 18/64] Update FedBN baseline to new format (#2608)

---
 baselines/fedbn/.gitignore                    |   2 +
 baselines/fedbn/LICENSE                       | 202 ++++++++++++++
 baselines/fedbn/README.md                     | 159 +++++++++++
 baselines/fedbn/_static/train_loss.png        | Bin 0 -> 52231 bytes
 baselines/fedbn/docs/multirun_plot.ipynb      | 220 +++++++++++++++
 baselines/fedbn/fedbn/__init__.py             |   1 +
 baselines/fedbn/fedbn/client.py               | 189 +++++++++++++
 baselines/fedbn/fedbn/conf/base.yaml          |  41 +++
 baselines/fedbn/fedbn/conf/client/fedavg.yaml |   4 +
 baselines/fedbn/fedbn/conf/client/fedbn.yaml  |   4 +
 baselines/fedbn/fedbn/dataset.py              | 263 ++++++++++++++++++
 baselines/fedbn/fedbn/dataset_preparation.py  |   8 +
 baselines/fedbn/fedbn/main.py                 |  86 ++++++
 baselines/fedbn/fedbn/models.py               | 114 ++++++++
 baselines/fedbn/fedbn/server.py               |   5 +
 baselines/fedbn/fedbn/strategy.py             |  85 ++++++
 baselines/fedbn/fedbn/utils.py                |  76 +++++
 baselines/fedbn/pyproject.toml                | 143 ++++++++++
 doc/source/ref-changelog.md                   |   2 +
 19 files changed, 1604 insertions(+)
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 create mode 100644 baselines/fedbn/LICENSE
 create mode 100644 baselines/fedbn/README.md
 create mode 100644 baselines/fedbn/_static/train_loss.png
 create mode 100644 baselines/fedbn/docs/multirun_plot.ipynb
 create mode 100644 baselines/fedbn/fedbn/__init__.py
 create mode 100644 baselines/fedbn/fedbn/client.py
 create mode 100644 baselines/fedbn/fedbn/conf/base.yaml
 create mode 100644 baselines/fedbn/fedbn/conf/client/fedavg.yaml
 create mode 100644 baselines/fedbn/fedbn/conf/client/fedbn.yaml
 create mode 100644 baselines/fedbn/fedbn/dataset.py
 create mode 100644 baselines/fedbn/fedbn/dataset_preparation.py
 create mode 100644 baselines/fedbn/fedbn/main.py
 create mode 100644 baselines/fedbn/fedbn/models.py
 create mode 100644 baselines/fedbn/fedbn/server.py
 create mode 100644 baselines/fedbn/fedbn/strategy.py
 create mode 100644 baselines/fedbn/fedbn/utils.py
 create mode 100644 baselines/fedbn/pyproject.toml

diff --git a/baselines/fedbn/.gitignore b/baselines/fedbn/.gitignore
new file mode 100644
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+outputs/
+multirun/
diff --git a/baselines/fedbn/LICENSE b/baselines/fedbn/LICENSE
new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/fedbn/LICENSE
@@ -0,0 +1,202 @@
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diff --git a/baselines/fedbn/README.md b/baselines/fedbn/README.md
new file mode 100644
index 000000000000..cc4f68b90e9e
--- /dev/null
+++ b/baselines/fedbn/README.md
@@ -0,0 +1,159 @@
+---
+title: "FedBN: Federated Learning on Non-IID Features via Local Batch Normalization"
+url: https://arxiv.org/abs/2102.07623
+labels: [data heterogeneity, feature shift, cross-silo]
+dataset: [MNIST, MNIST-M, SVHN, USPS, SynthDigits]
+---
+
+# FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
+
+> Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
+
+
+**Paper:** [arxiv.org/abs/2102.07623](https://arxiv.org/abs/2102.07623)
+
+**Authors:** Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, Qi Dou
+
+**Abstract:** The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy. In most cases, the assumption of independent and identically distributed samples across local clients does not hold for federated learning setups. Under this setting, neural network training performance may vary significantly according to the data distribution and even hurt training convergence. Most of the previous work has focused on a difference in the distribution of labels or client shifts. Unlike those settings, we address an important problem of FL, e.g., different scanners/sensors in medical imaging, different scenery distribution in autonomous driving (highway vs. city), where local clients store examples with different distributions compared to other clients, which we denote as feature shift non-iid. In this work, we propose an effective method that uses local batch normalization to alleviate the feature shift before averaging models. The resulting scheme, called FedBN, outperforms both classical FedAvg, as well as the state-of-the-art for non-iid data (FedProx) on our extensive experiments. These empirical results are supported by a convergence analysis that shows in a simplified setting that FedBN has a faster convergence rate than FedAvg.
+
+
+## About this baseline
+
+**What’s implemented:** Figure 3 in the paper: convergence in training loss comparing `FedBN` to `FedAvg` for five datasets.
+
+**Datasets:** Vision datasets including digits 0-9. These datasets are: [MNIST](https://ieeexplore.ieee.org/document/726791), [MNIST-M](https://arxiv.org/pdf/1505.07818.pdf), [SVHN](http://ufldl.stanford.edu/housenumbers/nips2011_housenumbers.pdf), [USPS](https://ieeexplore.ieee.org/document/291440), and [SynthDigits](https://arxiv.org/pdf/1505.07818.pdf).
+
+**Hardware Setup:** Using the default configurations, any machine with 8 CPU cores should be capable to run 100 rounds of FedAvg or FedBN in under 5 minutes. Therefore a GPU is not needed if you stick to the small model used in the paper and you limit clients to use a 10% of the data in each dataset (these are the default settings)
+
+**Contributors:** Meirui Jiang, Maria Boerner, Javier Fernandez-Marques
+
+
+## Experimental Setup
+
+**Task:** Image classification
+
+**Model:** A six-layer CNN with 14,219,210 parameters following the structure described in appendix D.2.
+
+**Dataset:**  This baseline makes use of the pre-processed partitions created and open source by the authors of the FedBN paper. You can read more about how those were created [here](https://github.com/med-air/FedBN). Follow the steps below in the `Environment Setup` section to download them. 
+
+
+A more detailed explanation of the datasets is given in the following table.
+
+|     | MNIST     | MNIST-M   | SVHN  |  USPS    | SynthDigits |
+|--- |---        |---        |---    |---            |---    |
+| data type| handwritten digits| MNIST modification randomly colored with colored patches| Street view house numbers | handwritten digits from envelopes by the U.S. Postal Service | Syntehtic digits Windows TM font varying the orientation, blur and stroke colors |
+| color | greyscale | RGB | RGB | greyscale | RGB |
+| pixelsize | 28x28 | 28 x 28 | 32 x32 | 16 x16 | 32 x32 |
+| labels | 0-9 | 0-9 | 1-10 | 0-9 | 1-10 |
+| number of trainset | 60.000 | 60.000 | 73.257 | 9,298 | 50.000 |
+| number of testset| 10.000 | 10.000 | 26.032 | - | - |
+| image shape | (28,28) | (28,28,3) | (32,32,3) | (16,16) | (32,32,3) |
+
+
+**Training Hyperparameters:** By default (i.e. if you don't override anything in the config) these main hyperparameters used are shown in the table below. For a complete list of hyperparameters, please refer to the config files in `fedbn/conf`.
+
+| Description | Value |
+| ----------- | ----- |
+| rounds | 10 |
+| num_clients | 5 |
+| strategy_fraction_fit | 1.0 |
+| strategy.fraction_evaluate | 0.0 |
+| training samples per client| 743 |
+| lr | 10E-2 |
+| local epochs | 1 |
+| loss | cross entropy loss |
+| optimizer | SGD |
+| client_resources.num_cpu | 2 |
+| client_resources.num_gpus | 0.0 |
+
+## Environment Setup
+
+To construct the Python environment, simply run:
+
+```bash
+# Set directory to use python 3.10 (install with `pyenv install <version>` if you don't have it)
+pyenv local 3.10.6
+
+# Tell poetry to use python3.10
+poetry env use 3.10.6
+
+# Install
+poetry install
+```
+
+Before running the experiments you'll need to download the five datasets for this baseline. We'll be using the pre-processed datasets created by the `FedBN` authors. Download the dataset from [here](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155149226_link_cuhk_edu_hk/EV1YgHfFC4RKjw06NL4JMdgBMU21CegM12SpXrSmGjt3XA?e=XK2rFs) and move the file into a new directory named `data`.
+```bash
+mkdir data
+mv <path/to/your/digit_dataset.zip> data/
+
+# now uncompress the zipfile
+cd data && unzip digit_dataset.zip
+cd data ..
+```
+
+## Running the Experiments
+
+First, activate your environment via `poetry shell`. The commands below show how to run the experiments and modify some of its key hyperparameters via the cli. Each time you run an experiment, the log and results will be stored inside `outputs/<date>/<time>`. Please refer to [the Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) to learn more about Flower Simulation.
+
+```bash
+# run with default arguments
+python -m fedbn.main
+
+# by default, the experiments run in CPU-only mode
+# allow for 5 clients in parallel on a gpu by doing
+python -m fedbn.main client_resources.num_gpus=0.2
+
+# By default, federated evaluation is disabled. Therefore the only metrics
+# returned to the server are those related to the training set
+# If you want to enable federated evaluation set `fraction_evaluate=1.0` to involve all clients
+python -m fedbn.main strategy.fraction_evaluate=1 # your code will run slower
+
+# adjust hyperparameters like the number of rounds or batch size like this
+python -m fedbn.main num_rounds=100 dataset.batch_size
+
+# increase the number of clients like this (note this should be a multiple 
+# of the number of dataset you involve in the experiment -- 5 by default)
+# this means that without changing other hyperparameters, you can only have
+# either 5,10,15,20,25,30,35,40,45 or 50 clients
+python -m fedbn.main num_clients=20
+
+# by default clients get assigned a 10th of the data in a dataset
+# this is because the datasets you downloaded were pre-processed by the authors
+# of the FedBN paper. They created 10 partitions.
+# You can increase the amount of partitions each client gets by increasing dataset.percent
+# Please note that as you increase that value, the maximum number of clients
+# you can have in your experiment gets reduced (this is because partitions are fixed and
+# can't be -- unless you add support for it -- partitioned into smaller ones)
+python -m fedbn.main dataset.percent=0.2 # max allowed is 25 clients
+
+# run with FedAvg clients leaving the rest default
+python -m fedbn.main client=fedavg
+```
+
+## Limitations
+
+The pre-processing of the five datasets provided by the authors, imposes some limitations on the number of clients that can be spawned for the experiment. Naturally, this limitation can be circumvented if you edit the code, and in particular the `dataset.DigitsDataset` constructor. The aforementioned limitation happens because each dataset is partitioned into 10 disjoint sets and a 'DigitsDataset' can only be constructed by concatenating any set of such partitions (at least one, at most all 10). _How does the limitation manifest?_ Given that we have 5 datasets, if a client just takes one partition, a FL setup can accommodate 50 clients, one using a different partition. But, if you want for instance each client have 3 partitions of the same dataset (yes, clients can only hold data of one dataset) then the maximum number of clients gets reduced to 20. Following this logic you can see that if a client wants to use all 10 partitions of a given dataset, then only 5 clients can participate in the experiment. 
+
+Another limitation in the current implementation is that there should be the same number of clients for each dataset. Also, all clients should contain the same number of partitions of their respective datasets. You can remove these constrain my editing `dataset.get_data()`.
+
+The function `dataset.get_data()` contains a few `assert` that should make it easy find the right valid set of arguments for `num_clients` given the values of `dataset.percent` and `dataset.to_include`.
+
+## Expected Results
+
+Replicate the results shown below by running the following command. First ensureing you have activated your environment.
+
+```bash
+
+# Reproduces the results in Table 3 of the paper.
+python -m fedbn.main --multirun num_rounds=100 client=fedavg,fedbn
+
+# If you want to repeat the same experiment 5 times with different random seeds (none fixed in the code)
+# adding '+repeat_num=range(5)' adds an aunxhiliary new parameter (not used in the code) that forces the --multirun
+# to run a total of 2x5 configs
+python -m fedbn.main --multirun num_rounds=100 client=fedavg,fedbn client_resources.num_gpus=0.2 '+repeat_num=range(5)'
+
+# then use the notebook in docs/multirun_plot.ipynb to create the plot below
+# The results show each strategy averaged for the N runs you run the experiment
+```
+
+![FedBn vs FedAvg on all datasets](_static/train_loss.png)
diff --git a/baselines/fedbn/_static/train_loss.png b/baselines/fedbn/_static/train_loss.png
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diff --git a/baselines/fedbn/docs/multirun_plot.ipynb b/baselines/fedbn/docs/multirun_plot.ipynb
new file mode 100644
index 000000000000..6314e0033b9d
--- /dev/null
+++ b/baselines/fedbn/docs/multirun_plot.ipynb
@@ -0,0 +1,220 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle, yaml\n",
+    "from pathlib import Path\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def saveFig(name, fig):\n",
+    "    fig.savefig(\n",
+    "        name,\n",
+    "        dpi=None,\n",
+    "        facecolor=fig.get_facecolor(),\n",
+    "        edgecolor=\"none\",\n",
+    "        orientation=\"portrait\",\n",
+    "        format=\"png\",\n",
+    "        transparent=False,\n",
+    "        bbox_inches=\"tight\",\n",
+    "        pad_inches=0.2,\n",
+    "        metadata=None,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_pickle_and_config(path_to_pickle):\n",
+    "    with open(path_to_pickle, \"rb\") as handle:\n",
+    "        data = pickle.load(handle)\n",
+    "\n",
+    "    config_path = Path(path_to_pickle).parent / \".hydra\" / \"config.yaml\"\n",
+    "    with open(config_path, \"r\") as file:\n",
+    "        config = yaml.safe_load(file)\n",
+    "\n",
+    "    return data, config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fuse_by_dataset(losses):\n",
+    "    \"\"\"Transform per-round history (list of dicts) into\n",
+    "    \n",
+    "    a single dict, with values as lists.\"\"\"\n",
+    "    fussed_losses = {}\n",
+    "\n",
+    "    for _, loss_dict in losses:\n",
+    "        for k,v in loss_dict.items():\n",
+    "            if k in fussed_losses:\n",
+    "                fussed_losses[k].append(v)\n",
+    "            else:\n",
+    "                fussed_losses[k] = [v]\n",
+    "    return fussed_losses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_multirun_data(path_multirun):\n",
+    "    \"\"\"Given a path to a multirun directory, this loads the history of all runs\"\"\"\n",
+    "    res_list = []\n",
+    "    for results in list(Path(path_multirun).glob(\"**/history.pkl\")):\n",
+    "        data, config = read_pickle_and_config(results)\n",
+    "        pre_train_loss = data['history'].metrics_distributed_fit['pre_train_losses']\n",
+    "        fussed_losses = fuse_by_dataset(pre_train_loss)\n",
+    "        res_list.append({'strategy': config['client']['client_label'], 'train_losses': fussed_losses})\n",
+    "    return res_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here replace with the path of the multi run you just comleted\n",
+    "all_losses = process_multirun_data('../multirun/2023-11-15/22-28-28')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def average_by_client_type(all_fused_lossed):\n",
+    "    \"\"\"If there are multliple runs for the same strategy add them up,\n",
+    "    \n",
+    "    average them later. This is useful if you run the `--multirun` running\n",
+    "    more than one time the same configuration.\"\"\"\n",
+    "    \n",
+    "    # identify how many unique clients were used\n",
+    "    to_plot = {}\n",
+    "    for res in all_fused_lossed:\n",
+    "        strategy = res['strategy']\n",
+    "        if strategy not in to_plot:\n",
+    "            to_plot[strategy] = {}\n",
+    "        \n",
+    "        for dataset, train_loss in res['train_losses'].items():\n",
+    "            if dataset in to_plot[strategy]:\n",
+    "                to_plot[strategy][dataset]['train_loss'] += np.array(train_loss)\n",
+    "                to_plot[strategy][dataset]['run_count'] += 1\n",
+    "            else:\n",
+    "                to_plot[strategy][dataset] = {'train_loss': np.array(train_loss)}\n",
+    "                to_plot[strategy][dataset]['run_count'] = 1\n",
+    "\n",
+    "    # print(to_plot)\n",
+    "    return to_plot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "to_plot = average_by_client_type(all_losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['SVHN', 'MNIST_M', 'SynthDigits', 'USPS', 'MNIST'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datasets = to_plot[list(to_plot.keys())[0]].keys()\n",
+    "print(datasets)\n",
+    "\n",
+    "num_datasets = len(datasets)\n",
+    "fig, axs = plt.subplots(figsize=(14,4), nrows=1, ncols=num_datasets)\n",
+    "\n",
+    "\n",
+    "for s_id, (strategy, results) in enumerate(to_plot.items()):\n",
+    "    for i, dataset in enumerate(datasets):\n",
+    "        loss = results[dataset]['train_loss']/results[dataset]['run_count']\n",
+    "        axs[i].plot(range(len(loss)), loss, label=strategy)\n",
+    "        axs[i].set_xlabel('Round')\n",
+    "        if i == 0:\n",
+    "            axs[i].set_ylabel('Train Loss')\n",
+    "\n",
+    "        axs[i].legend()\n",
+    "\n",
+    "        if s_id==0:\n",
+    "            axs[i].grid()\n",
+    "            axs[i].set_title(dataset)\n",
+    "            axs[i].set_xticks(np.arange(0,100+1, 25))\n",
+    "\n",
+    "saveFig('train_loss.png', fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fedbn-LmgZrzC0-py3.10",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/baselines/fedbn/fedbn/__init__.py b/baselines/fedbn/fedbn/__init__.py
new file mode 100644
index 000000000000..a5e567b59135
--- /dev/null
+++ b/baselines/fedbn/fedbn/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/fedbn/fedbn/client.py b/baselines/fedbn/fedbn/client.py
new file mode 100644
index 000000000000..be3b4b8f54a7
--- /dev/null
+++ b/baselines/fedbn/fedbn/client.py
@@ -0,0 +1,189 @@
+"""FedAvg and FedNB clients."""
+
+import pickle
+from collections import OrderedDict
+from pathlib import Path
+from typing import Callable, Dict, List, Tuple
+
+import flwr as fl
+import torch
+from flwr.common.typing import NDArrays, Scalar
+from hydra.utils import instantiate
+from omegaconf import DictConfig
+from torch.utils.data import DataLoader
+
+from fedbn.models import CNNModel, test, train
+
+
+class FlowerClient(fl.client.NumPyClient):
+    """A standar FlowerClient. This base class.
+
+    is what plain FedAvg clients do.
+    """
+
+    def __init__(
+        self,
+        model: CNNModel,
+        trainloader: DataLoader,
+        testloader: DataLoader,
+        dataset_name: str,
+        **kwargs,  # pylint: disable=unused-argument
+    ) -> None:
+        self.trainloader = trainloader
+        self.testloader = testloader
+        self.dataset_name = dataset_name
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.model = model.to(self.device)
+
+    def get_parameters(self, config) -> NDArrays:
+        """Return model parameters as a list of NumPy ndarrays w or w/o.
+
+        using BNlayers.
+        """
+        # Return all model parameters as a list of NumPy ndarrays
+        return [val.cpu().numpy() for _, val in self.model.state_dict().items()]
+
+    def set_parameters(self, parameters: NDArrays) -> None:
+        """Set model parameters from a list of NumPy ndarrays Exclude the BN.
+
+        layer if available.
+        """
+        params_dict = zip(self.model.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
+        self.model.load_state_dict(state_dict, strict=True)
+
+    def fit(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[NDArrays, int, Dict]:
+        """Set model parameters, train model, return updated model parameters."""
+        self.set_parameters(parameters)
+
+        # evaluate the state of the global model on the train set; the loss returned
+        # is what's reported in Fig3 in the FedBN paper (what this baseline focuses
+        # in reproducing)
+        pre_train_loss, pre_train_acc = test(
+            self.model, self.trainloader, device=self.device
+        )
+
+        # train model on local dataset
+        loss, acc = train(
+            self.model,
+            self.trainloader,
+            epochs=1,
+            device=self.device,
+        )
+
+        # construct metrics to return to server
+        fl_round = config["round"]
+        metrics = {
+            "dataset_name": self.dataset_name,
+            "round": fl_round,
+            "accuracy": acc,
+            "loss": loss,
+            "pre_train_loss": pre_train_loss,
+            "pre_train_acc": pre_train_acc,
+        }
+
+        return (
+            self.get_parameters({}),
+            len(self.trainloader.dataset),
+            metrics,
+        )
+
+    def evaluate(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[float, int, Dict]:
+        """Set model parameters, evaluate model on local test dataset, return result."""
+        self.set_parameters(parameters)
+
+        loss, accuracy = test(self.model, self.testloader, device=self.device)
+        return (
+            float(loss),
+            len(self.testloader.dataset),
+            {"loss": loss, "accuracy": accuracy, "dataset_name": self.dataset_name},
+        )
+
+
+class FedBNFlowerClient(FlowerClient):
+    """Similar to FlowerClient but this is used by FedBN clients."""
+
+    def __init__(self, bn_state_dir: Path, client_id: int, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.bn_state_dir = bn_state_dir
+        self.bn_state_pkl = bn_state_dir / f"client_{client_id}.pkl"
+
+    def _save_bn_statedict(self) -> None:
+        """Save contents of state_dict related to BN layers."""
+        bn_state = {
+            name: val.cpu().numpy()
+            for name, val in self.model.state_dict().items()
+            if "bn" in name
+        }
+
+        with open(self.bn_state_pkl, "wb") as handle:
+            pickle.dump(bn_state, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+    def _load_bn_statedict(self) -> Dict[str, torch.tensor]:
+        """Load pickle with BN state_dict and return as dict."""
+        with open(self.bn_state_pkl, "rb") as handle:
+            data = pickle.load(handle)
+        bn_stae_dict = {k: torch.tensor(v) for k, v in data.items()}
+        return bn_stae_dict
+
+    def get_parameters(self, config) -> NDArrays:
+        """Return model parameters as a list of NumPy ndarrays w or w/o using BN.
+
+        layers.
+        """
+        # first update bn_state_dir
+        self._save_bn_statedict()
+        # Excluding parameters of BN layers when using FedBN
+        return [
+            val.cpu().numpy()
+            for name, val in self.model.state_dict().items()
+            if "bn" not in name
+        ]
+
+    def set_parameters(self, parameters: NDArrays) -> None:
+        """Set model parameters from a list of NumPy ndarrays Exclude the bn layer if.
+
+        available.
+        """
+        keys = [k for k in self.model.state_dict().keys() if "bn" not in k]
+        params_dict = zip(keys, parameters)
+        state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
+        self.model.load_state_dict(state_dict, strict=False)
+
+        # now also load from bn_state_dir
+        if self.bn_state_pkl.exists():  # it won't exist in the first round
+            bn_state_dict = self._load_bn_statedict()
+            self.model.load_state_dict(bn_state_dict, strict=False)
+
+
+def gen_client_fn(
+    client_data: List[Tuple[DataLoader, DataLoader, str]],
+    client_cfg: DictConfig,
+    model_cfg: DictConfig,
+    bn_state_dir: Path,
+) -> Callable[[str], FlowerClient]:
+    """Return a function that will be called to instantiate the cid-th client."""
+
+    def client_fn(cid: str) -> FlowerClient:
+        """Create a Flower client representing a single organization."""
+        # Instantiate model
+        net = instantiate(model_cfg)
+
+        # Note: each client gets a different trainloader/valloader, so each client
+        # will train and evaluate on their own unique data
+        trainloader, valloader, dataset_name = client_data[int(cid)]
+        return instantiate(
+            client_cfg,
+            model=net,
+            trainloader=trainloader,
+            testloader=valloader,
+            dataset_name=dataset_name,
+            bn_state_dir=bn_state_dir,
+            client_id=int(cid),
+        )
+
+    return client_fn
diff --git a/baselines/fedbn/fedbn/conf/base.yaml b/baselines/fedbn/fedbn/conf/base.yaml
new file mode 100644
index 000000000000..13d1831cd25c
--- /dev/null
+++ b/baselines/fedbn/fedbn/conf/base.yaml
@@ -0,0 +1,41 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+defaults:
+  - client: fedbn
+
+num_rounds: 10
+num_clients: 5 # any multiple of 5 between 5 and 50 (both included)
+               # this value should be choosen taking dataset.percent into consideration
+               # for example if percent=0.2 then at most we can have 25 clients, each
+               # using two data partitions of the same dataset.
+
+dataset:
+  to_include: [MNIST, SVHN, USPS, SynthDigits, MNIST_M]
+  data_path: 'data'
+  num_clients: ${num_clients}
+  percent: 0.1 # fraction of partitions to include for each client.
+               # Note there are 10 partitions per dataset.
+               # therefore percent should be any of {0.1, 0.2, .... 0.8, 0.9, 1.0}
+  batch_size: 32
+
+model:
+  _target_: fedbn.models.CNNModel
+
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  fraction_fit: 1.0
+  fraction_evaluate: 0.0
+  min_available_clients: ${num_clients}
+  on_fit_config_fn:
+    _target_: fedbn.strategy.get_on_fit_config
+  evaluate_metrics_aggregation_fn:
+    _target_: fedbn.strategy.get_metrics_aggregation_fn
+  fit_metrics_aggregation_fn:
+    _target_: fedbn.strategy.get_metrics_aggregation_fn
+
+client_resources:
+  num_cpus: 2
+  num_gpus: 0.0
diff --git a/baselines/fedbn/fedbn/conf/client/fedavg.yaml b/baselines/fedbn/fedbn/conf/client/fedavg.yaml
new file mode 100644
index 000000000000..a08fccc6d24e
--- /dev/null
+++ b/baselines/fedbn/fedbn/conf/client/fedavg.yaml
@@ -0,0 +1,4 @@
+---
+# standard FedAvg Flower Client
+_target_: fedbn.client.FlowerClient
+client_label: FedAvg
\ No newline at end of file
diff --git a/baselines/fedbn/fedbn/conf/client/fedbn.yaml b/baselines/fedbn/fedbn/conf/client/fedbn.yaml
new file mode 100644
index 000000000000..0f0e2a05cbfb
--- /dev/null
+++ b/baselines/fedbn/fedbn/conf/client/fedbn.yaml
@@ -0,0 +1,4 @@
+---
+# standard FedBN Flower Client
+_target_: fedbn.client.FedBNFlowerClient
+client_label: FedBN
\ No newline at end of file
diff --git a/baselines/fedbn/fedbn/dataset.py b/baselines/fedbn/fedbn/dataset.py
new file mode 100644
index 000000000000..49fdad166430
--- /dev/null
+++ b/baselines/fedbn/fedbn/dataset.py
@@ -0,0 +1,263 @@
+"""Digits dataset."""
+import os
+from random import shuffle
+from typing import List, Optional, Tuple
+
+import numpy as np
+from omegaconf import DictConfig
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+
+
+class DigitsDataset(Dataset):
+    """Split datasets."""
+
+    total_partitions: int = 10
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        data_path: str,
+        channels: int,
+        train: bool,
+        partitions: Optional[List[int]] = None,
+        transform=None,
+    ):
+        if train and partitions is not None:
+            # construct dataset by loading one or more partitions
+            self.images, self.labels = np.load(
+                os.path.join(
+                    data_path,
+                    f"partitions/train_part{partitions[0]}.pkl",
+                ),
+                allow_pickle=True,
+            )
+            for part in partitions[1:]:
+                images, labels = np.load(
+                    os.path.join(
+                        data_path,
+                        f"partitions/train_part{part}.pkl",
+                    ),
+                    allow_pickle=True,
+                )
+                self.images = np.concatenate([self.images, images], axis=0)
+                self.labels = np.concatenate([self.labels, labels], axis=0)
+
+        else:
+            self.images, self.labels = np.load(
+                os.path.join(data_path, "test.pkl"), allow_pickle=True
+            )
+
+        self.transform = transform
+        self.channels = channels
+        self.labels = self.labels.squeeze()
+
+    def __len__(self) -> int:
+        """Return number of images."""
+        return self.images.shape[0]
+
+    def __getitem__(self, idx):
+        """Return a transformed example of the dataset."""
+        image = self.images[idx]
+        label = self.labels[idx]
+        if self.channels == 1:
+            image = Image.fromarray(image, mode="L")
+        elif self.channels == 3:
+            image = Image.fromarray(image, mode="RGB")
+        else:
+            raise ValueError(f"{self.channels} channel is not allowed.")
+
+        if self.transform is not None:
+            image = self.transform(image)
+
+        return image, label
+
+
+def load_partition(
+    dataset: str, path_to_data: str, partition_indx: List[int], batch_size: int
+) -> Tuple[DataLoader, DataLoader]:
+    """Load 'MNIST', 'SVHN', 'USPS', 'SynthDigits', 'MNIST_M' for the training and test.
+
+    data to simulate a partition.
+    """
+    data_path = os.path.join(path_to_data, dataset)
+
+    if dataset == "MNIST":
+        transform = transforms.Compose(
+            [
+                transforms.Grayscale(num_output_channels=3),
+                transforms.ToTensor(),
+                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            ]
+        )
+
+        trainset = DigitsDataset(
+            data_path=data_path,
+            channels=1,
+            partitions=partition_indx,
+            train=True,
+            transform=transform,
+        )
+        testset = DigitsDataset(
+            data_path=data_path,
+            channels=1,
+            train=False,
+            transform=transform,
+        )
+
+    elif dataset == "SVHN":
+        transform = transforms.Compose(
+            [
+                transforms.Resize([28, 28]),
+                transforms.ToTensor(),
+                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            ]
+        )
+
+        trainset = DigitsDataset(
+            data_path=data_path,
+            channels=3,
+            partitions=partition_indx,
+            train=True,
+            transform=transform,
+        )
+        testset = DigitsDataset(
+            data_path=data_path,
+            channels=3,
+            train=False,
+            transform=transform,
+        )
+
+    elif dataset == "USPS":
+        transform = transforms.Compose(
+            [
+                transforms.Resize([28, 28]),
+                transforms.Grayscale(num_output_channels=3),
+                transforms.ToTensor(),
+                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            ]
+        )
+
+        trainset = DigitsDataset(
+            data_path=data_path,
+            channels=1,
+            partitions=partition_indx,
+            train=True,
+            transform=transform,
+        )
+        testset = DigitsDataset(
+            data_path=data_path,
+            channels=1,
+            train=False,
+            transform=transform,
+        )
+
+    elif dataset == "SynthDigits":
+        transform = transforms.Compose(
+            [
+                transforms.Resize([28, 28]),
+                transforms.ToTensor(),
+                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            ]
+        )
+
+        trainset = DigitsDataset(
+            data_path=data_path,
+            channels=3,
+            partitions=partition_indx,
+            train=True,
+            transform=transform,
+        )
+        testset = DigitsDataset(
+            data_path=data_path,
+            channels=3,
+            train=False,
+            transform=transform,
+        )
+
+    elif dataset == "MNIST_M":
+        transform = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            ]
+        )
+
+        trainset = DigitsDataset(
+            data_path=data_path,
+            channels=3,
+            partitions=partition_indx,
+            train=True,
+            transform=transform,
+        )
+        testset = DigitsDataset(
+            data_path=data_path,
+            channels=3,
+            train=False,
+            transform=transform,
+        )
+
+    else:
+        raise NotImplementedError(f"dataset: {dataset} is not available")
+
+    trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
+    testloader = DataLoader(testset, batch_size=batch_size, shuffle=False)
+
+    return trainloader, testloader
+
+
+# pylint: disable=too-many-locals
+def get_data(dataset_cfg: DictConfig) -> List[Tuple[DataLoader, DataLoader, str]]:
+    """Generate dataloaders for each client."""
+    client_data = []
+    d_cfg = dataset_cfg
+
+    allowed_percent = (np.arange(1, 11) / 10).tolist()
+    assert d_cfg.percent in allowed_percent, (
+        f"'dataset.percent' should be in {allowed_percent}."
+        "\nThis is because the trainset is pre-partitioned into 10 disjoint sets."
+    )
+    max_expected_clients = len(d_cfg.to_include) * 1 / d_cfg.percent
+
+    num_clients_step = len(d_cfg.to_include)
+    possible_client_configs = np.arange(
+        num_clients_step,
+        max_expected_clients + num_clients_step,
+        num_clients_step,
+        dtype=np.int32,
+    ).tolist()
+    assert d_cfg.num_clients in possible_client_configs, (
+        f"'dataset.num_clients' should be in {possible_client_configs}."
+        "\n this is because you include {len(d_cfg.to_include)} datasets "
+        f"(i.e. {d_cfg.to_include}) and each should be used by the same number"
+        " of clients. The values of `num_clients` also depend on the"
+        "'dataset.percent' you chose."
+    )
+
+    num_clients_per_dataset = d_cfg.num_clients // num_clients_step
+    total_partitions = (
+        10  # each dataset was pre-processed by the authors and split into 10 partitions
+    )
+    num_parts = int(d_cfg.percent * total_partitions)
+
+    for dataset_name in dataset_cfg.to_include:
+        parts = list(range(total_partitions))
+        shuffle(parts)
+        for i in range(num_clients_per_dataset):
+            parts_for_client = parts[i * num_parts : (i + 1) * num_parts]
+            print(f"{dataset_name = } | {parts_for_client = }")
+            trainloader, testloader = load_partition(
+                dataset_name,
+                path_to_data=d_cfg.data_path,
+                partition_indx=parts_for_client,
+                batch_size=d_cfg.batch_size,
+            )
+
+            client_data.append((trainloader, testloader, dataset_name))
+
+    # Ensure there is an entry in the list for each client
+    assert (
+        len(client_data) == d_cfg.num_clients
+    ), f"{len(client_data) = } | {d_cfg.num_clients = }"
+
+    return client_data
diff --git a/baselines/fedbn/fedbn/dataset_preparation.py b/baselines/fedbn/fedbn/dataset_preparation.py
new file mode 100644
index 000000000000..0120ab9dfb0f
--- /dev/null
+++ b/baselines/fedbn/fedbn/dataset_preparation.py
@@ -0,0 +1,8 @@
+"""Handle the dataset partitioning and (optionally) complex downloads.
+
+Please add here all the necessary logic to either download, uncompress, pre/post-process
+your dataset (or all of the above). If the desired way of running your baseline is to
+first download the dataset and partition it and then run the experiments, please
+uncomment the lines below and tell us in the README.md (see the "Running the Experiment"
+block) that this file should be executed first.
+"""
diff --git a/baselines/fedbn/fedbn/main.py b/baselines/fedbn/fedbn/main.py
new file mode 100644
index 000000000000..8942a175052d
--- /dev/null
+++ b/baselines/fedbn/fedbn/main.py
@@ -0,0 +1,86 @@
+"""Create and connect the building blocks for your experiments; start the simulation.
+
+It includes processioning the dataset, instantiate strategy, specify how the global
+model is going to be evaluated, etc. At the end, this script saves the results.
+"""
+# these are the basic packages you'll need here
+# feel free to remove some if aren't needed
+import pickle
+from pathlib import Path
+
+import flwr as fl
+import hydra
+from hydra.core.hydra_config import HydraConfig
+from hydra.utils import instantiate
+from omegaconf import DictConfig, OmegaConf
+
+from fedbn.client import gen_client_fn
+from fedbn.dataset import get_data
+from fedbn.utils import quick_plot
+
+
+@hydra.main(config_path="conf", config_name="base", version_base=None)
+def main(cfg: DictConfig) -> None:
+    """Run the baseline.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    # 1. Print parsed config
+    print(OmegaConf.to_yaml(cfg))
+
+    # Hydra automatically creates an output directory
+    # Let's retrieve it and save some results there
+    save_path = HydraConfig.get().runtime.output_dir
+
+    # For FedBN clients we need to persist the state of the BN
+    # layers across rounds. In Simulation clients are statess
+    # so everything not communicated to the server (as it is the
+    # case as with params in BN layers of FedBN clients) is lost
+    # once a client completes its training. An upcoming version of
+    # Flower suports stateful clients
+    bn_states = Path(save_path) / "bn_states"
+    bn_states.mkdir()
+
+    # 2. Prepare your dataset
+    # please ensure you followed the README.md and you downloaded the
+    # pre-processed dataset suplied by the authors of the FedBN paper
+    client_data_loaders = get_data(cfg.dataset)
+
+    # 3. Define your client generation function
+    client_fn = gen_client_fn(client_data_loaders, cfg.client, cfg.model, bn_states)
+
+    # 4. Define your strategy
+    strategy = instantiate(cfg.strategy)
+
+    # 5. Start Simulation
+    history = fl.simulation.start_simulation(
+        client_fn=client_fn,
+        num_clients=cfg.num_clients,
+        config=fl.server.ServerConfig(num_rounds=cfg.num_rounds),
+        client_resources={
+            "num_cpus": cfg.client_resources.num_cpus,
+            "num_gpus": cfg.client_resources.num_gpus,
+        },
+        strategy=strategy,
+    )
+
+    # 6. Save your results
+    print("................")
+    print(history)
+
+    # save results as a Python pickle using a file_path
+    # the directory created by Hydra for each run
+    data = {"history": history}
+    history_path = f"{str(save_path)}/history.pkl"
+    with open(history_path, "wb") as handle:
+        pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+    # simple plot
+    quick_plot(history_path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/fedbn/fedbn/models.py b/baselines/fedbn/fedbn/models.py
new file mode 100644
index 000000000000..efe982905123
--- /dev/null
+++ b/baselines/fedbn/fedbn/models.py
@@ -0,0 +1,114 @@
+"""Define our models, and training and eval functions.
+
+If your model is 100% off-the-shelf (e.g. directly from torchvision without requiring
+modifications) you might be better off instantiating your  model directly from the Hydra
+config. In this way, swapping your model for  another one can be done without changing
+the python code at all
+"""
+
+from typing import Tuple
+
+import torch
+from torch import nn
+
+
+# pylint: disable=too-many-instance-attributes
+class CNNModel(nn.Module):
+    """CNN model proposed in the FedBN paper."""
+
+    def __init__(self, num_classes=10):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 64, 5, 1, 2)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.conv2 = nn.Conv2d(64, 64, 5, 1, 2)
+        self.bn2 = nn.BatchNorm2d(64)
+        self.conv3 = nn.Conv2d(64, 128, 5, 1, 2)
+        self.bn3 = nn.BatchNorm2d(128)
+
+        self.fc1 = nn.Linear(6272, 2048)
+        self.bn4 = nn.BatchNorm1d(2048)
+        self.fc2 = nn.Linear(2048, 512)
+        self.bn5 = nn.BatchNorm1d(512)
+        self.fc3 = nn.Linear(512, num_classes)
+
+    def forward(self, x):
+        """Forward pass."""
+        x = nn.functional.relu(self.bn1(self.conv1(x)))
+        x = nn.functional.max_pool2d(x, 2)
+
+        x = nn.functional.relu(self.bn2(self.conv2(x)))
+        x = nn.functional.max_pool2d(x, 2)
+
+        x = nn.functional.relu(self.bn3(self.conv3(x)))
+
+        x = x.view(x.shape[0], -1)
+
+        x = self.fc1(x)
+        x = self.bn4(x)
+        x = nn.functional.relu(x)
+
+        x = self.fc2(x)
+        x = self.bn5(x)
+        x = nn.functional.relu(x)
+
+        x = self.fc3(x)
+        return x
+
+
+# pylint: disable=too-many-locals
+def train(model, traindata, epochs, device) -> Tuple[float, float]:
+    """Train the network."""
+    # Define loss and optimizer
+    criterion = nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
+
+    # Train the network
+    model.to(device)
+    model.train()
+    total_loss = 0.0
+    for _ in range(epochs):  # loop over the dataset multiple times
+        total = 0.0
+        correct = 0
+        for _i, data in enumerate(traindata, 0):
+            images, labels = data[0].to(device), data[1].to(device)
+
+            # zero the parameter gradients
+            optimizer.zero_grad()
+
+            # forward + backward + optimize
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            # print statistics
+            total_loss += loss.item()
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+
+    return total_loss / len(traindata), correct / total
+
+
+def test(model, testdata, device) -> Tuple[float, float]:
+    """Validate the network on the entire test set."""
+    # Define loss and metrics
+    criterion = nn.CrossEntropyLoss()
+    correct = 0
+    total = 0
+    loss = 0.0
+
+    # Evaluate the network
+    model.to(device)
+    model.eval()
+    with torch.no_grad():
+        for data in testdata:
+            images, labels = data[0].to(device), data[1].to(device)
+            outputs = model(images)
+            loss += criterion(outputs, labels).item()
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+    accuracy = correct / total
+    loss = loss / len(testdata)
+    return loss, accuracy
diff --git a/baselines/fedbn/fedbn/server.py b/baselines/fedbn/fedbn/server.py
new file mode 100644
index 000000000000..2fd7d42cde5a
--- /dev/null
+++ b/baselines/fedbn/fedbn/server.py
@@ -0,0 +1,5 @@
+"""Create global evaluation function.
+
+Optionally, also define a new Server class (please note this is not needed in most
+settings).
+"""
diff --git a/baselines/fedbn/fedbn/strategy.py b/baselines/fedbn/fedbn/strategy.py
new file mode 100644
index 000000000000..0f457daedb33
--- /dev/null
+++ b/baselines/fedbn/fedbn/strategy.py
@@ -0,0 +1,85 @@
+"""Optionally define a custom strategy.
+
+Needed only when the strategy is not yet implemented in Flower or because you want to
+extend or modify the functionality of an existing strategy.
+"""
+from typing import Dict, List, Tuple
+
+from flwr.common.typing import Metrics
+
+
+def _update_dict(dictionary, key, value):
+    if key in dictionary:
+        dictionary[key] += value
+    else:
+        dictionary[key] = value
+
+
+# Define metric aggregation function
+def get_metrics_aggregation_fn():
+    """Return function to compute metrics average.
+
+    It is used for both fit() metrics and evaluate() metrics.
+    """
+
+    def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+        """Compute per-dataset average accuracy and loss."""
+        # compute per-dataset accuracy and loss
+        totals: Dict[str, int] = {}
+        accuracies: Dict[str, float] = {}
+        losses: Dict[str, float] = {}
+        pre_train_accuracies: Dict[str, float] = {}
+        pre_train_losses: Dict[str, float] = {}
+
+        for num_examples, mett in metrics:
+            dataset_name = mett["dataset_name"]
+
+            _update_dict(totals, dataset_name, num_examples)
+            _update_dict(accuracies, dataset_name, num_examples * mett["accuracy"])
+            _update_dict(losses, dataset_name, num_examples * mett["loss"])
+            if "pre_train_acc" in mett:
+                _update_dict(
+                    pre_train_accuracies,
+                    dataset_name,
+                    num_examples * mett["pre_train_acc"],
+                )
+            if "pre_train_loss" in mett:
+                _update_dict(
+                    pre_train_losses,
+                    dataset_name,
+                    num_examples * mett["pre_train_loss"],
+                )
+
+        # now normalise each metric per-dataset by the amount of total data
+        # used in the round
+        accuracies.update((k, v / totals[k]) for k, v in accuracies.items())
+        losses.update((k, v / totals[k]) for k, v in losses.items())
+
+        to_return = {"accuracy": accuracies, "losses": losses}
+
+        if "pre_train_acc" in metrics[0][1]:
+            pre_train_accuracies.update(
+                (k, v / totals[k]) for k, v in pre_train_accuracies.items()
+            )
+            to_return = {**to_return, "pre_train_accuracies": pre_train_accuracies}
+        if "pre_train_loss" in metrics[0][1]:
+            pre_train_losses.update(
+                (k, v / totals[k]) for k, v in pre_train_losses.items()
+            )
+            to_return = {**to_return, "pre_train_losses": pre_train_losses}
+
+        return to_return  # type: ignore
+
+    return weighted_average
+
+
+def get_on_fit_config():
+    """Return a config (a dict) to be sent to clients during fit()."""
+
+    def fit_config_fn(server_round: int):
+        # resolve and convert to python dict
+        fit_config = {}
+        fit_config["round"] = server_round  # add round info
+        return fit_config
+
+    return fit_config_fn
diff --git a/baselines/fedbn/fedbn/utils.py b/baselines/fedbn/fedbn/utils.py
new file mode 100644
index 000000000000..5a6458d105b8
--- /dev/null
+++ b/baselines/fedbn/fedbn/utils.py
@@ -0,0 +1,76 @@
+"""Define any utility function.
+
+They are not directly relevant to  the other (more FL specific) python modules. For
+example, you may define here things like: loading a model from a checkpoint, saving
+results, plotting.
+"""
+
+import pickle
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import yaml
+
+
+def save_fig(name, fig):
+    """Save matplotlib plot."""
+    fig.savefig(
+        name,
+        dpi=None,
+        facecolor=fig.get_facecolor(),
+        edgecolor="none",
+        orientation="portrait",
+        format="png",
+        transparent=False,
+        bbox_inches="tight",
+        pad_inches=0.2,
+        metadata=None,
+    )
+
+
+def _read_pickle_and_config(path_to_pickle):
+    with open(path_to_pickle, "rb") as handle:
+        data = pickle.load(handle)
+
+    config_path = Path(path_to_pickle).parent / ".hydra" / "config.yaml"
+    with open(config_path, "r") as file:
+        config = yaml.safe_load(file)
+
+    return data, config
+
+
+def _fuse_by_dataset(losses):
+    fussed_losses = {}
+    losses[0][1].keys()
+
+    for _, loss_dict in losses:
+        for k, val in loss_dict.items():
+            if k in fussed_losses:
+                fussed_losses[k].append(val)
+            else:
+                fussed_losses[k] = [val]
+    return fussed_losses
+
+
+def quick_plot(path_to_pickled_history: str):
+    """Plot training loss for all datasets in the same plot."""
+    data, config = _read_pickle_and_config(path_to_pickled_history)
+
+    pre_train_loss = data["history"].metrics_distributed_fit["pre_train_losses"]
+
+    fussed_losses = _fuse_by_dataset(pre_train_loss)
+
+    fig, _ = plt.subplots(figsize=(10, 6))
+    for dataset, loss in fussed_losses.items():
+        plt.plot(range(len(loss)), loss, label=dataset)
+        plt.title(
+            f"client: {config['client']['client_label']}\n num_clients:"
+            " {config['num_clients']}, percent: {config['dataset']['percent']}"
+        )
+        plt.legend()
+        plt.grid()
+        plt.xlabel("Round")
+        plt.ylabel("Train Loss")
+
+    dir_path = Path(path_to_pickled_history).parent
+    save_fig(f"{dir_path}/train_loss.png", fig)
diff --git a/baselines/fedbn/pyproject.toml b/baselines/fedbn/pyproject.toml
new file mode 100644
index 000000000000..0e46a33360db
--- /dev/null
+++ b/baselines/fedbn/pyproject.toml
@@ -0,0 +1,143 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "fedbn"
+version = "1.0.0"
+description = "Flower Baselines"
+license = "Apache-2.0"
+authors = ["The Flower Authors <hello@flower.dev>"]
+readme = "README.md"
+homepage = "https://flower.dev"
+repository = "https://github.com/adap/flower"
+documentation = "https://flower.dev"
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Mathematics",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Typing :: Typed",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10.0, <3.11.0"
+flwr = { extras = ["simulation"], version = "1.5.0" }
+hydra-core = "1.3.2"
+matplotlib = "3.7.1"
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+tqdm = "^4.66.1"
+
+[tool.poetry.dev-dependencies]
+isort = "==5.11.5"
+black = "==23.1.0"
+docformatter = "==1.5.1"
+mypy = "==1.4.1"
+pylint = "==2.8.2"
+flake8 = "==3.9.2"
+pytest = "==6.2.4"
+pytest-watch = "==4.2.0"
+ruff = "==0.0.272"
+types-requests = "==2.27.7"
+types-pyyaml = "6.0.12.12"
+virtualenv = "^20.21.0"
+
+[tool.isort]
+line_length = 88
+indent = "    "
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+
+[tool.black]
+line-length = 88
+target-version = ["py38", "py39", "py310", "py311"]
+
+[tool.pytest.ini_options]
+minversion = "6.2"
+addopts = "-qq"
+testpaths = [
+    "flwr_baselines",
+]
+
+[tool.mypy]
+ignore_missing_imports = true
+strict = false
+plugins = "numpy.typing.mypy_plugin"
+
+[tool.pylint."MESSAGES CONTROL"]
+disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
+good-names = "i,j,k,_,x,y,X,Y"
+signature-mutators="hydra.main.main"
+
+[tool.pylint.typecheck]
+generated-members="numpy.*, torch.*, tensorflow.*"
+
+[[tool.mypy.overrides]]
+module = [
+    "importlib.metadata.*",
+    "importlib_metadata.*",
+]
+follow_imports = "skip"
+follow_imports_for_stubs = true
+disallow_untyped_calls = false
+
+[[tool.mypy.overrides]]
+module = "torch.*"
+follow_imports = "skip"
+follow_imports_for_stubs = true
+
+[tool.docformatter]
+wrap-summaries = 88
+wrap-descriptions = 88
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+select = ["D", "E", "F", "W", "B", "ISC", "C4"]
+fixable = ["D", "E", "F", "W", "B", "ISC", "C4"]
+ignore = ["B024", "B027"]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "proto",
+]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 6b2432ab34fa..299228170b80 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -46,6 +46,8 @@
 
   - niid-Bench [#2428](https://github.com/adap/flower/pull/2428)
 
+  - FedBN [#2608](https://github.com/adap/flower/pull/2608)
+
 - **Update Flower Examples** ([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425), [#2526](https://github.com/adap/flower/pull/2526))
 
 - **General updates to baselines** ([#2301](https://github.com/adap/flower/pull/2301), [#2305](https://github.com/adap/flower/pull/2305), [#2307](https://github.com/adap/flower/pull/2307), [#2327](https://github.com/adap/flower/pull/2327), [#2435](https://github.com/adap/flower/pull/2435))

From f674d2677d968ed27a24a869a2db79b589860f40 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Thu, 16 Nov 2023 13:34:50 +0000
Subject: [PATCH 19/64] Remove old FedBN (#2609)

---
 README.md                                     |   2 -
 baselines/README.md                           |   4 +-
 baselines/doc/source/how-to-use-baselines.rst |   2 +-
 .../fedbn/convergence_rate/README.md          | 189 --------
 .../fedbn/convergence_rate/__init__.py        |   0
 .../fedbn/convergence_rate/client.py          | 402 ------------------
 ...nvergence_rate_FedBN_FedAvg_comparison.png | Bin 34158 -> 0 bytes
 ...nvergence_rate_Flower_FedBN_comparison.png | Bin 36446 -> 0 bytes
 .../fedbn/convergence_rate/evaluation_plot.py |  47 --
 .../fedbn/convergence_rate/run.sh             |  24 --
 .../fedbn/convergence_rate/server.py          |  16 -
 .../fedbn/convergence_rate/utils/__init__.py  |   0
 .../fedbn/convergence_rate/utils/cnn_model.py |  48 ---
 .../convergence_rate/utils/data_download.py   |  67 ---
 .../utils/data_download_raw.py                | 186 --------
 .../convergence_rate/utils/data_preprocess.py | 278 ------------
 .../convergence_rate/utils/data_utils.py      |  87 ----
 .../convergence_rate/utils/mnistm/__init__.py |   0
 .../utils/mnistm/create_mnistm.py             |  71 ----
 19 files changed, 3 insertions(+), 1420 deletions(-)
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/README.md
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/__init__.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/client.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/convergence_rate_FedBN_FedAvg_comparison.png
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/convergence_rate_Flower_FedBN_comparison.png
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/evaluation_plot.py
 delete mode 100755 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/run.sh
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/server.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/__init__.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/cnn_model.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_download.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_download_raw.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_preprocess.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_utils.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/mnistm/__init__.py
 delete mode 100644 baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/mnistm/create_mnistm.py

diff --git a/README.md b/README.md
index 002d16066e78..4f148d2445d9 100644
--- a/README.md
+++ b/README.md
@@ -97,8 +97,6 @@ Flower Baselines is a collection of community-contributed experiments that repro
   - [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist)
 - [FedProx](https://arxiv.org/abs/1812.06127):
   - [MNIST](https://github.com/adap/flower/tree/main/baselines/fedprox/)
-- [FedBN: Federated Learning on non-IID Features via Local Batch Normalization](https://arxiv.org/abs/2102.07623):
-  - [Convergence Rate](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate)
 - [Adaptive Federated Optimization](https://arxiv.org/abs/2003.00295):
   - [CIFAR-10/100](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization)
 
diff --git a/baselines/README.md b/baselines/README.md
index 3b30ff1a9eaf..a18c0553b2b4 100644
--- a/baselines/README.md
+++ b/baselines/README.md
@@ -1,14 +1,14 @@
 # Flower Baselines
 
 
-> We are changing the way we structure the Flower baselines. While we complete the transition to the new format, you can still find the existing baselines in the `flwr_baselines` directory. Currently, you can make use of baselines for [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist), [FedProx](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedprox_mnist), [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization), [FedBN](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate), and [LEAF-FEMNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist). 
+> We are changing the way we structure the Flower baselines. While we complete the transition to the new format, you can still find the existing baselines in the `flwr_baselines` directory. Currently, you can make use of baselines for [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist), [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization), and [LEAF-FEMNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist). 
 
 > The documentation below has been updated to reflect the new way of using Flower baselines.
 
 
 ## Structure
 
-Each baseline in this directory is fully self-contained in terms of source code it its own directory. In addition, each baseline uses its very own Python environment as designed by the contributors of such baseline in order to replicate the experiments in the paper. Each baseline directory contains the following structure:
+Each baseline in this directory is fully self-contained in terms of source code in its own directory. In addition, each baseline uses its very own Python environment as designed by the contributors of such baseline in order to replicate the experiments in the paper. Each baseline directory contains the following structure:
 
 ```bash
 baselines/<baseline-name>/
diff --git a/baselines/doc/source/how-to-use-baselines.rst b/baselines/doc/source/how-to-use-baselines.rst
index b89c17b17a98..ed47438ad5a9 100644
--- a/baselines/doc/source/how-to-use-baselines.rst
+++ b/baselines/doc/source/how-to-use-baselines.rst
@@ -3,7 +3,7 @@ Use Baselines
 
 .. warning::
   We are changing the way we structure the Flower baselines. While we complete the transition to the new format, you can still find the existing baselines and use them: `baselines (old) <https://github.com/adap/flower/tree/main/baselines/flwr_baselines>`_.
-  Currently, you can make use of baselines for `FedAvg <https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist>`_, `FedProx <https://github.com/adap/flower/tree/main/baselines/fedprox>`_, `FedOpt <https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization>`_, `FedBN <https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate>`_, and `LEAF-FEMNIST <https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist>`_.
+  Currently, you can make use of baselines for `FedAvg <https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist>`_, `FedOpt <https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization>`_,  and `LEAF-FEMNIST <https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist>`_.
 
   The documentation below has been updated to reflect the new way of using Flower baselines.
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/README.md b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/README.md
deleted file mode 100644
index 2b718afd7dd4..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/README.md
+++ /dev/null
@@ -1,189 +0,0 @@
-# FedBN Baseline - Convergence Rate
-
-## Experiment Introduction
-
-The **FedBN - Convergence Rate** baseline is based on the paper [FEDBN: FEDERATED LEARNING ON NON-IID FEATURES VIA LOCAL BATCH NORMALIZATION](https://arxiv.org/pdf/2102.07623.pdf) and reproduces the results presented in *Chapter 5 - Convergence Rate (Figure 3)*. The implementation is based on the Flower federated learning framework. This experiment uses 5 completely different image datasets of digits to emulate a non-IID data distribution over the different clients. The experiment therefore uses 5 clients for the training. The local training is set up to perform 1 epoch and it uses a CNN model together with the SGD optimizer (cross-entropy loss).
-
-## Dataset 
-
-### General Overview
-
-The following 5 different datasets are used to simulate a non-IID data distribution over 5 clients:
-
-* [MNIST](https://ieeexplore.ieee.org/document/726791)
-* [MNIST-M]((https://arxiv.org/pdf/1505.07818.pdf))
-* [SVHN](http://ufldl.stanford.edu/housenumbers/nips2011_housenumbers.pdf)
-* [USPS](https://ieeexplore.ieee.org/document/291440)
-* [SynthDigits](https://arxiv.org/pdf/1505.07818.pdf)
-
-A more detailed explanation of the datasets is given in the following table.
-
-|     | MNIST     | MNIST-M   | SVHN  |  USPS    | SynthDigits |
-|--- |---        |---        |---    |---            |---    |
-| data type| handwritten digits| MNIST modification randomly colored with colored patches| Street view house numbers | handwritten digits from envelopes by the U.S. Postal Service | Syntehtic digits Windows TM font varying the orientation, blur and stroke colors |
-| color | greyscale | RGB | RGB | greyscale | RGB |
-| pixelsize | 28x28 | 28 x 28 | 32 x32 | 16 x16 | 32 x32 |
-| labels | 0-9 | 0-9 | 1-10 | 0-9 | 1-10 |
-| number of trainset | 60.000 | 60.000 | 73.257 | 9,298 | 50.000 |
-| number of testset| 10.000 | 10.000 | 26.032 | - | - |
-| image shape | (28,28) | (28,28,3) | (32,32,3) | (16,16) | (32,32,3) |
-
-### Dataset Download
-
-The [FedBN](https://arxiv.org/pdf/2102.07623.pdf) authors prepared a preprocessed dataset on their GitHub repository. It is available [here](https://github.com/med-air/FedBN). Please download the dataset, save it in a `/data` directory, and unzip it.
-
-The training data contains only 7438 samples and it is split into 10 files, but only one file is used for the **FedBN: Convergence Rate** baseline. Therefore, 743 samples are used for the local training.
-
-Run the following commands to download and preprocess the original data:
-
-```bash
-# download data (will create a directory in ./path)
-python utils/data_download.py
-
-# preprocess
-python utils/data_preprocess.py
-```
-
-All the datasets (with the exception of SynthDigits) can be downloaded from the original sources:
-
-```bash
-# download
-python utils/data_download_raw.py 
-
-# preprocess
-python utils/data_preprocess.py
-```
-
-## Training Setup
-
-### CNN Architecture
-
-The CNN architecture is detailed in the paper and used to create the **FedBN - Convergence Rate** baseline.
-
-| Layer | Details|
-| ----- | ------ |
-| 1 | Conv2D(3,64, 5,1,2) <br/> BN(64), ReLU, MaxPool2D(2,2)  |
-| 2 | Conv2D(64, 64, 5, 1, 2) <br/> BN(64), ReLU, MaxPool2D(2,2) |
-| 3 | Conv2D(64, 128, 5, 1, 2) <br/> BN(128), ReLU |
-| 4 | FC(6272, 2048) <br/> BN(2048), ReLU |
-| 5 | FC(2048, 512) <br/> BN(512), ReLU |
-| 6 | FC(512, 10) |
-
-### Training Paramaters
-
-| Description | Value |
-| ----------- | ----- |
-| training samples | 743 |
-| mu | 10E-2 |
-| local epochs | 1 |
-| loss | cross entropy loss |
-| optimizer | SGD |
-
-## Running the Experiment
-
-Before running any part of the experiment, [please get the required data](dataset-download), and place it in the `/data` directory.
-
-As soon as you have downloaded the data you are ready to start the baseline experiment. The baseline implementation is split across different files:
-
-* cnn_model.py
-* client.py
-* server.py
-* run.sh
-* utils/data_utils.py
-* evaluation_plot.py
-
-In order to run the experiment, you simply make `run.sh` executable and run it:
-
-```bash
-chmod +x run.sh
-# train the CNN with FedAvg strategy
-./run.sh fedavg
-# train the CNN with FedBN strategy
-./run.sh fedbn
-```
-
-The `run.sh` script first creates the files in which the evaluation results are saved and starts `server.py` and 5 clients in parallel with `client.py`. As explained before, each client loads a different dataset. The clients save the evaluation results after the parameters were sent from the server to the client and right before the local training. The saved parameters are included in a dict with the following information:
-
-```python
-test_dict =  {"dataset": self.num_examples["dataset"], "fl_round" : fl_round, "strategy": self.mode, "test_loss": loss, "test_accuracy": accuracy}
-```
-
-The `utils/data_utils.py` script prepares/loads the data for the training and `cnn_model.py` defines the [CNN model architecture](#cnn-architecture). This baseline only uses one single file with 743 samples from the downloaded dataset.
-
-If you want to compare the results of both strategy runs (FedAvg and FedBN) you can run:
-
-```bash
-# create the evalutation plot
-python evaluation_plot.py
-```
-
-This will create a plot `convergence_rate.png` including the train loss after the server aggregation from a certain client for the FedAvg and FedBN stretegy. A noticeable difference between both stretagies is visible for the SVHN dataset. The train loss has a more stable and steeper decrease when using FedBN over FedAvg.
-
-![Illustration of convergence rate on the SVHN client trained with Flower using FedBN and FedAvg.](convergence_rate_FedBN_FedAvg_comparison.png)
-
-This baseline was created to reproduce the [FedBN implementation available on GitHub](https://github.com/med-air/FedBN). The following diagram shows the convergence rate of the SVHN client with same strategy, FedBN, using the Flower implementation and the original FedBN implementation. The loss decrease is very similar in both cases.
-
-![Illustration of convergence rate on the SVHN client trained with Flower and the traditional FedBN code.](convergence_rate_Flower_FedBN_comparison.png)
-
-### Server
-
-This baseline compares Federated Averaging (FedAvg) with Federated Batch Normalization (FedBN). In both cases, we are using FedAvg on the server-side. All model parameters are sent from the client to the server and aggregated. However, in the case of FedBN, we are setting up the client to exclude batch norm layers from the transmission to the server. FedBN is therefore a strategy that only requires changes on the client-side.
-
-The server is kept very simple and remains the same in both settings. We are using FedAvg on the server-side with the parameters `min_fit_clients`, `min_eval_clients`, and `min_available_clients`. All are set to the value `5` since we have five clients to be trained and evaluated in each FL round. All in all, the *FedBN* paper runs 600 FL rounds that can be implemented as follows.     
-
-```python
-import flwr as fl
-
-if __name__ == "__main__":
-    strategy = fl.server.strategy.FedAvg(
-        min_fit_clients=5,
-        min_eval_clients=5,
-        min_available_clients=5,
-    )
-    fl.server.start_server(server_address="[::]:8080", config={"num_rounds": 100}, strategy=strategy)
-
-```
-
-### Client
-
-The client is a little more complex. However, it can be separated into different parts. The main parts are:
-
-* `load_partition()`
-    * load the right dataset
-* `train()`
-    * perfom the local training
-* `test()`
-    * evaluate the training results
-* `FlowerClient(fl.client.NumPyClient)`
-    * start the Flower client
-* `main()`
-    * start all previous process in a main() file
-
-The `load_partition()` function loads the datasets saved in the `/data` dierctory.
-
-You can directly see that the training and evaluation process is defined within the client. We are using PyTorch to train and evaluate the model with the parameters described in the section [Training Setup](#training-setup). 
-
-The Flower client `FlowerClient(fl.client.NumPyClient)` implements the usual methods:
-
-* get_paramaters()
-* set_parameters()
-* fit()
-* evaluate()
-
-Let us take a closer look at `set_parameters()` in order to understand the difference between FedAvg and FedBN:
-
-```python 
-def set_parameters(self, parameters: List[np.ndarray])-> None:
-    self.model.train()
-    if self.mode == 'fedbn':
-        keys = [k for k in self.model.state_dict().keys() if "bn" not in k]
-        params_dict = zip(keys, parameters)
-        state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
-        self.model.load_state_dict(state_dict, strict=False)
-    else:
-        params_dict = zip(self.model.state_dict().keys(), parameters)
-        state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
-        self.model.load_state_dict(state_dict, strict=True)
-```
-
-You can see that the clients update all layers of the PyTorch model with the values received from the server when using the plain FedAvg strategy (including batch norm layers). However, in the case of FedBN, the parameters for the batch norm layers (`bn`) are excluded. 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/__init__.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/__init__.py
deleted file mode 100644
index e69de29bb2d1..000000000000
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/client.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/client.py
deleted file mode 100644
index db2ab647e1ba..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/client.py
+++ /dev/null
@@ -1,402 +0,0 @@
-"""FedBN client."""
-
-
-import argparse
-import json
-from collections import OrderedDict
-from typing import Dict, Tuple
-
-import flwr as fl
-import torch
-from flwr.common.typing import NDArrays, Scalar
-from torch import nn
-from torchvision import transforms
-
-from .utils.cnn_model import CNNModel
-from .utils.data_utils import DigitsDataset
-
-FL_ROUND = 0
-
-eval_list = []
-
-
-# pylint: disable=no-member
-DEVICE: str = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-# pylint: enable=no-member
-
-
-# mypy: allow-any-generics
-# pylint: disable= too-many-arguments, too-many-locals, global-statement
-class FlowerClient(fl.client.NumPyClient):
-    """Flower client implementing image classification using PyTorch."""
-
-    def __init__(
-        self,
-        model: CNNModel,
-        trainloader: torch.utils.data.DataLoader,
-        testloader: torch.utils.data.DataLoader,
-        num_examples: Dict,
-        mode: str,
-    ) -> None:
-        self.model = model
-        self.trainloader = trainloader
-        self.testloader = testloader
-        self.num_examples = num_examples
-        self.mode = mode
-
-    def get_parameters(self, config) -> NDArrays:
-        """Return model parameters as a list of NumPy ndarrays w or w/o using
-        BN layers."""
-        self.model.train()
-        # pylint: disable = no-else-return
-        if self.mode == "fedbn":
-            # Excluding parameters of BN layers when using FedBN
-            return [
-                val.cpu().numpy()
-                for name, val in self.model.state_dict().items()
-                if "bn" not in name
-            ]
-        else:
-            # Return all model parameters as a list of NumPy ndarrays
-            return [val.cpu().numpy() for _, val in self.model.state_dict().items()]
-
-    def set_parameters(self, parameters: NDArrays) -> None:
-        """Set model parameters from a list of NumPy ndarrays Exclude the bn
-        layer if available."""
-        self.model.train()
-        # pylint: disable=not-callable
-        if self.mode == "fedbn":
-            keys = [k for k in self.model.state_dict().keys() if "bn" not in k]
-            params_dict = zip(keys, parameters)
-            state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
-            self.model.load_state_dict(state_dict, strict=False)
-        else:
-            params_dict = zip(self.model.state_dict().keys(), parameters)
-            state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
-            self.model.load_state_dict(state_dict, strict=True)
-        # pylint: enable=not-callable
-
-    def fit(
-        self, parameters: NDArrays, config: Dict[str, Scalar]
-    ) -> Tuple[NDArrays, int, Dict]:
-        """Set model parameters, train model, return updated model
-        parameters."""
-        self.set_parameters(parameters)
-        test_loss, test_accuracy = test(
-            self.model, self.num_examples["dataset"], self.trainloader, device=DEVICE
-        )
-        test_dict = {
-            "dataset": self.num_examples["dataset"],
-            "fl_round": FL_ROUND,
-            "strategy": self.mode,
-            "train_loss": test_loss,
-            "train_accuracy": test_accuracy,
-        }
-        loss, accuracy = train(
-            self.model,
-            self.trainloader,
-            self.num_examples["dataset"],
-            epochs=1,
-            device=DEVICE,
-        )
-        eval_list.append(test_dict)
-        return (
-            self.get_parameters({}),
-            self.num_examples["trainset"],
-            {"loss": loss, "accuracy": accuracy},
-        )
-
-    def evaluate(
-        self, parameters: NDArrays, config: Dict[str, Scalar]
-    ) -> Tuple[float, int, Dict]:
-        """Set model parameters, evaluate model on local test dataset, return
-        result."""
-        self.set_parameters(parameters)
-        global FL_ROUND
-        loss, accuracy = test(
-            self.model, self.num_examples["dataset"], self.testloader, device=DEVICE
-        )
-        test_dict = {
-            "dataset": self.num_examples["dataset"],
-            "fl_round": FL_ROUND,
-            "strategy": self.mode,
-            "test_loss": loss,
-            "test_accuracy": accuracy,
-        }
-        eval_list.append(test_dict)
-        FL_ROUND += 1
-        return (
-            float(loss),
-            self.num_examples["testset"],
-            {"loss": loss, "accuracy": accuracy},
-        )
-
-
-def load_partition(
-    dataset: str,
-) -> Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader, Dict]:
-    """Load 'MNIST', 'SVHN', 'USPS', 'SynthDigits', 'MNIST-M' for the training
-    and test data to simulate a partition."""
-
-    if dataset == "MNIST":
-        print(f"Load {dataset} dataset")
-
-        transform = transforms.Compose(
-            [
-                transforms.Grayscale(num_output_channels=3),
-                transforms.ToTensor(),
-                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-            ]
-        )
-
-        trainset = DigitsDataset(
-            data_path="data/MNIST",
-            channels=1,
-            percent=0.1,
-            train=True,
-            transform=transform,
-        )
-        testset = DigitsDataset(
-            data_path="data/MNIST",
-            channels=1,
-            percent=0.1,
-            train=False,
-            transform=transform,
-        )
-
-    elif dataset == "SVHN":
-        print(f"Load {dataset} dataset")
-
-        transform = transforms.Compose(
-            [
-                transforms.Resize([28, 28]),
-                transforms.ToTensor(),
-                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-            ]
-        )
-
-        trainset = DigitsDataset(
-            data_path="data/SVHN",
-            channels=3,
-            percent=0.1,
-            train=True,
-            transform=transform,
-        )
-        testset = DigitsDataset(
-            data_path="data/SVHN",
-            channels=3,
-            percent=0.1,
-            train=False,
-            transform=transform,
-        )
-
-    elif dataset == "USPS":
-        print(f"Load {dataset} dataset")
-
-        transform = transforms.Compose(
-            [
-                transforms.Resize([28, 28]),
-                transforms.Grayscale(num_output_channels=3),
-                transforms.ToTensor(),
-                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-            ]
-        )
-
-        trainset = DigitsDataset(
-            data_path="data/USPS",
-            channels=1,
-            percent=0.1,
-            train=True,
-            transform=transform,
-        )
-        testset = DigitsDataset(
-            data_path="data/USPS",
-            channels=1,
-            percent=0.1,
-            train=False,
-            transform=transform,
-        )
-
-    elif dataset == "SynthDigits":
-        print(f"Load {dataset} dataset")
-
-        transform = transforms.Compose(
-            [
-                transforms.Resize([28, 28]),
-                transforms.ToTensor(),
-                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-            ]
-        )
-
-        trainset = DigitsDataset(
-            data_path="data/SynthDigits/",
-            channels=3,
-            percent=0.1,
-            train=True,
-            transform=transform,
-        )
-        testset = DigitsDataset(
-            data_path="data/SynthDigits/",
-            channels=3,
-            percent=0.1,
-            train=False,
-            transform=transform,
-        )
-
-    elif dataset == "MNIST-M":
-        print(f"Load {dataset} dataset")
-
-        transform = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-            ]
-        )
-
-        trainset = DigitsDataset(
-            data_path="data/MNIST_M/",
-            channels=3,
-            percent=0.1,
-            train=True,
-            transform=transform,
-        )
-        testset = DigitsDataset(
-            data_path="data/MNIST_M/",
-            channels=3,
-            percent=0.1,
-            train=False,
-            transform=transform,
-        )
-
-    else:
-        print("No valid dataset available")
-
-    num_examples = {
-        "dataset": dataset,
-        "trainset": len(trainset),
-        "testset": len(testset),
-    }
-
-    print(f"Loaded {dataset} dataset with {num_examples} samples. Good Luck!")
-
-    trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
-    testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)
-
-    return trainloader, testloader, num_examples
-
-
-def train(model, traindata, dataset, epochs, device) -> Tuple[float, float]:
-    """Train the network."""
-    # Define loss and optimizer
-    criterion = nn.CrossEntropyLoss()
-    optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
-
-    print(
-        f"Training {dataset} dataset with {epochs} local epoch(s) w/ {len(traindata)} batches each"
-    )
-
-    # Train the network
-    model.to(device)
-    model.train()
-    for epoch in range(epochs):  # loop over the dataset multiple times
-        running_loss = 0.0
-        total = 0.0
-        correct = 0
-        for i, data in enumerate(traindata, 0):
-            images, labels = data[0].to(device), data[1].to(device)
-
-            # zero the parameter gradients
-            optimizer.zero_grad()
-
-            # forward + backward + optimize
-            outputs = model(images)
-            loss = criterion(outputs, labels)
-            loss.backward()
-            optimizer.step()
-
-            # print statistics
-            running_loss += loss.item()
-            _, predicted = torch.max(outputs.data, 1)  # pylint: disable=no-member
-            total += labels.size(0)
-            correct += (predicted == labels).sum().item()
-            loss = running_loss
-            accuracy = correct / total
-            if i == len(traindata) - 1:  # print every 100 mini-batches
-                accuracy = correct / total
-                loss_batch = running_loss / len(traindata)
-                print(
-                    f"Train Dataset {dataset} with [{epoch+1}, {i+1}] \
-                    loss: {loss_batch} accuracy: {accuracy}"
-                )
-                running_loss = 0.0
-        loss = loss / len(traindata)
-    return loss, accuracy
-
-
-def test(model, dataset, testdata, device) -> Tuple[float, float]:
-    """Validate the network on the entire test set."""
-    # Define loss and metrics
-    criterion = nn.CrossEntropyLoss()
-    correct = 0
-    total = 0
-    loss = 0.0
-
-    # Evaluate the network
-    model.to(device)
-    model.eval()
-    with torch.no_grad():
-        for data in testdata:
-            images, labels = data[0].to(device), data[1].to(device)
-            outputs = model(images)
-            loss += criterion(outputs, labels).item()
-            _, predicted = torch.max(outputs.data, 1)  # pylint: disable=no-member
-            total += labels.size(0)
-            correct += (predicted == labels).sum().item()
-    accuracy = correct / total
-    loss = loss / len(testdata)
-    print(f"Dataset {dataset} with evaluation loss: {loss}")
-    return loss, accuracy
-
-
-def main() -> None:
-    """Load data, start FlowerClient."""
-
-    # Parse command line argument `partition` (type of dataset) and `mode` (type of strategy)
-    parser = argparse.ArgumentParser(description="Flower")
-    parser.add_argument(
-        "--partition",
-        type=str,
-        choices=["MNIST", "SVHN", "USPS", "SynthDigits", "MNIST-M"],
-        required=True,
-    )
-    parser.add_argument(
-        "--mode",
-        type=str,
-        choices=["fedbn", "fedavg"],
-        required=True,
-        default="fedbn",
-    )
-    args = parser.parse_args()
-
-    # Load model
-    model = CNNModel().to(DEVICE).train()
-
-    # Load data
-    trainloader, testloader, num_examples = load_partition(args.partition)
-
-    # Perform a single forward pass to properly initialize BatchNorm
-    _ = model(next(iter(trainloader))[0].to(DEVICE))
-
-    # Start client
-    client = FlowerClient(model, trainloader, testloader, num_examples, args.mode)
-    print("Start client of dataset", num_examples["dataset"])
-    fl.client.start_numpy_client(server_address="[::]:8000", client=client)
-    # Save train and evaluation loss and accuracy in json file
-    with open(
-        f"results/{args.partition}_{args.mode}_results.json", mode="r+"
-    ) as eval_file:
-        json.dump(eval_list, eval_file)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/convergence_rate_FedBN_FedAvg_comparison.png b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/convergence_rate_FedBN_FedAvg_comparison.png
deleted file mode 100644
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diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/convergence_rate_Flower_FedBN_comparison.png b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/convergence_rate_Flower_FedBN_comparison.png
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diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/evaluation_plot.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/evaluation_plot.py
deleted file mode 100644
index 38429d002f02..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/evaluation_plot.py
+++ /dev/null
@@ -1,47 +0,0 @@
-"""This code takes the evalution results from the directory results/ and
-creates a plot to compare e.g. fedbn with fedavg."""
-
-
-import json
-
-import matplotlib.pyplot as plt  # type: ignore
-
-fedavg_step_number = []
-fedavg_loss = []
-fedbn_step_number = []
-fedbn_loss = []
-
-
-def get_evaluation_numbers() -> None:
-    """Open the json files to get the evaluation results."""
-    with open("results/SVHN_fedavg_results.json") as fedavg_file:
-        fedavg_list = json.load(fedavg_file)
-    with open("results/SVHN_fedbn_results.json") as fedbn_file:
-        fedbn_list = json.load(fedbn_file)
-    for item in fedavg_list:
-        if "train_loss" in item:
-            fedavg_step_number.append(item["fl_round"])
-            fedavg_loss.append(item["train_loss"])
-    for item in fedbn_list:
-        if "train_loss" in item:
-            fedbn_step_number.append(item["fl_round"])
-            fedbn_loss.append(item["train_loss"])
-
-
-def main() -> None:
-    """Plot evaluation results."""
-    get_evaluation_numbers()
-    # pylint: disable= unused-variable, invalid-name
-    fig, ax = plt.subplots()
-    fedavg = ax.plot(fedavg_step_number, fedavg_loss, label="FedAvg")
-    fedbn = ax.plot(fedbn_step_number, fedbn_loss, label="FedBN")
-    ax.legend()
-    plt.axis([-3, 100, -0.1, 2.5])
-    plt.ylabel("Training loss")
-    plt.xlabel("Number of FL round")
-    plt.title("SVHN")
-    plt.savefig("convergence_rate.png")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/run.sh b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/run.sh
deleted file mode 100755
index 2a291781419d..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/run.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/bin/bash
-mode=${1:-"fedavg"}
-
-echo "Start Training with $mode"
-mkdir -p results
-
-python3 server.py &
-sleep 5 # Sleep for 5s to give the server enough time to start
-
-for i in 'MNIST' 'SVHN' 'USPS' 'SynthDigits' 'MNIST-M'; do
-    touch "results/${i}_"$mode"_results.json"
-    sleep 5 
-done
-
-for i in 'MNIST' 'SVHN' 'USPS' 'SynthDigits' 'MNIST-M' ; do
-    echo "Starting client $i"
-    python3 client.py --partition=${i} --mode="$mode" &
-    sleep 5 &
-done
-
-# This will allow you to use CTRL+C to stop all background processes
-trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
-# Wait for all background processes to complete
-wait
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/server.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/server.py
deleted file mode 100644
index cadb6713a24a..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/server.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""Flower server example."""
-
-
-import flwr as fl
-
-if __name__ == "__main__":
-    strategy = fl.server.strategy.FedAvg(
-        min_fit_clients=5,
-        min_evaluate_clients=5,
-        min_available_clients=5,
-    )
-    fl.server.start_server(
-        server_address="[::]:8000",
-        config=fl.server.ServerConfig(num_rounds=2),
-        strategy=strategy,
-    )
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/__init__.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1..000000000000
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/cnn_model.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/cnn_model.py
deleted file mode 100644
index 99bb80a3d116..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/cnn_model.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""Define the model architecture."""
-
-
-from torch import nn
-
-
-# pylint: disable=unsubscriptable-object,too-many-instance-attributes
-class CNNModel(nn.Module):
-    """Model for benchmark experiment on Digits."""
-
-    def __init__(self, num_classes=10):
-        super().__init__()
-        self.conv1 = nn.Conv2d(3, 64, 5, 1, 2)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.conv2 = nn.Conv2d(64, 64, 5, 1, 2)
-        self.bn2 = nn.BatchNorm2d(64)
-        self.conv3 = nn.Conv2d(64, 128, 5, 1, 2)
-        self.bn3 = nn.BatchNorm2d(128)
-
-        self.fc1 = nn.Linear(6272, 2048)
-        self.bn4 = nn.BatchNorm1d(2048)
-        self.fc2 = nn.Linear(2048, 512)
-        self.bn5 = nn.BatchNorm1d(512)
-        self.fc3 = nn.Linear(512, num_classes)
-
-    # pylint: disable=arguments-differ,invalid-name
-    def forward(self, x):
-        """Forward pass."""
-        x = nn.functional.relu(self.bn1(self.conv1(x)))
-        x = nn.functional.max_pool2d(x, 2)
-
-        x = nn.functional.relu(self.bn2(self.conv2(x)))
-        x = nn.functional.max_pool2d(x, 2)
-
-        x = nn.functional.relu(self.bn3(self.conv3(x)))
-
-        x = x.view(x.shape[0], -1)
-
-        x = self.fc1(x)
-        x = self.bn4(x)
-        x = nn.functional.relu(x)
-
-        x = self.fc2(x)
-        x = self.bn5(x)
-        x = nn.functional.relu(x)
-
-        x = self.fc3(x)
-        return x
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_download.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_download.py
deleted file mode 100644
index 5909b2d410a4..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_download.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""This code will download the required datasets from a Google Drive, save it
-in the directory ./data/data.zip and extracts it."""
-
-
-import os
-import zipfile
-from pathlib import Path
-from typing import Any
-
-import requests
-from tqdm import tqdm  # type: ignore
-
-# pylint: disable=invalid-name
-
-
-def download_file_from_google_drive(file_id: Any, destination: str) -> None:
-    """Download zip from Google drive."""
-    url = "https://docs.google.com/uc?export=download"
-
-    session = requests.Session()
-
-    response = session.get(url, params={"id": file_id}, stream=True)
-    token = get_confirm_token(response)
-
-    if token:
-        params = {"id": file_id, "confirm": token}
-        print("ID", params["id"])
-        response = session.get(url, params=params, stream=True)
-        total_length = response.headers.get("content-length")
-    print("Downloading...")
-    save_response_content(response, destination, total_length)  # type:ignore
-    print("Dowload done")
-
-
-# pylint: enable=invalid-name
-
-
-def get_confirm_token(response: Any) -> Any:
-    """Conform Google cookies."""
-    for key, value in response.cookies.items():
-        if key.startswith("download_warning"):
-            return value
-    return None
-
-
-def save_response_content(response: Any, destination: str, total_length: float) -> None:
-    """save data in the given data file."""
-    chunk_size = 32768
-
-    with open(destination, "wb") as download_file:
-        total_length = int(total_length)
-        for chunk in tqdm(
-            response.iter_content(chunk_size), total=int(total_length / chunk_size)
-        ):
-            if chunk:  # filter out keep-alive new chunks
-                download_file.write(chunk)
-
-
-if __name__ == "__main__":
-    Path("./data").mkdir(exist_ok=True)
-    FILE_ID = "1P8g7uHyVxQJPcBKE8TAzfdKbimpRbj0I"
-    DESTINATION = "data/data.zip"
-    download_file_from_google_drive(FILE_ID, DESTINATION)
-    print("Extracting...")
-    with zipfile.ZipFile(DESTINATION, "r") as zip_ref:
-        for file in tqdm(iterable=zip_ref.namelist(), total=len(zip_ref.namelist())):
-            zip_ref.extract(member=file, path=os.path.dirname(DESTINATION))
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_download_raw.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_download_raw.py
deleted file mode 100644
index 369c943d7884..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_download_raw.py
+++ /dev/null
@@ -1,186 +0,0 @@
-"""Download the raw datasets for MNIST, USPS, SVHN Create the MNISTM from MNIST
-And download the Synth Data (Syntehtic digits Windows TM font varying the
-orientation, blur and stroke colors).
-
-This dataset is already processed.
-"""
-
-
-import gzip
-import pickle
-import shutil
-from pathlib import Path
-from typing import Dict
-
-import numpy as np
-import torch
-import wget  # type: ignore
-from torchvision import datasets  # type: ignore
-
-from .mnistm import create_mnistm  # type: ignore
-
-# pylint: disable=invalid-name
-
-
-def decompress(infile, tofile):
-    """Take data file and unzip it."""
-
-    with open(infile, "rb") as inf, open(tofile, "w", encoding="utf8") as tof:
-        decom_str = gzip.decompress(inf.read()).decode("utf-8")
-        tof.write(decom_str)
-
-
-def download_all(data: Dict, out_dir: Path):
-    """Downloading datasets."""
-
-    for k, v in data.items():
-        print(f"Downloading: {k}\n")
-        wget.download(v, out=str(out_dir / k))
-
-
-def get_synthDigits(out_dir: Path):
-    """get synth dataset."""
-
-    if out_dir.exists():
-        print(f"Directory ({out_dir}) exists, skipping downloading SynthDigits.")
-        return
-
-    # pylint: disable=line-too-long
-    out_dir.mkdir()
-    data = {}
-    data[
-        "synth_train_32x32.mat"
-    ] = "https://github.com/domainadaptation/datasets/blob/master/synth/synth_train_32x32.mat?raw=true"
-    data[
-        "synth_test_32x32.mat"
-    ] = "https://github.com/domainadaptation/datasets/blob/master/synth/synth_test_32x32.mat?raw=true"
-    download_all(data, out_dir)
-    # pylint: disable=line-too-long
-
-    # How to proceed? It seems these `.mat` have no data. URLs found here:
-    # https://domainadaptation.org/api/salad.datasets.digits.html#module-salad.datasets.digits.synth
-
-
-def get_MNISTM(out_dir: Path):
-    """Creates MNISTM dataset as done by https://github.com/pumpikano/tf-
-    dann#build-mnist-m-dataset."""
-    # steps = 'https://github.com/pumpikano/tf-dann#build-mnist-m-dataset'
-    if out_dir.exists():
-        print(f"> Directory ({out_dir}) exists, skipping downloading MNISTM.")
-        return
-
-    out_dir.mkdir()
-    data = {}
-    data[
-        "BSR_bsds500.tgz"
-    ] = "http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz"
-    download_all(data, out_dir)
-
-    train = torch.load("./data/MNIST/training.pt")
-    test = torch.load("./data/MNIST/test.pt")
-    print("Building train set...")
-    train_labels = train[1]
-    train = create_mnistm.create_mnistm(train[0])
-    print("Building test set...")
-    test_labels = test[1]
-    test = create_mnistm.create_mnistm(test[0])
-    val_labels = np.zeros(0)
-    val = np.zeros([0, 28, 28, 3], np.uint8)
-
-    # Save dataset as pickle
-    with open(out_dir / "mnistm_data.pkl", "wb") as f:
-        pickle.dump(
-            {
-                "train": train,
-                "train_label": train_labels,
-                "test": test,
-                "test_label": test_labels,
-                "valid": val,
-                "valid_label": val_labels,
-            },
-            f,
-            pickle.HIGHEST_PROTOCOL,
-        )
-
-
-def get_USPS(out_dir: Path):
-    """get USPS data (handwritten digits from envelopes by the U.S.
-
-    Postal Service)
-    """
-
-    if out_dir.exists():
-        print(f"> Directory ({out_dir}) exists, skipping downloading USPS.")
-        return
-
-    out_dir.mkdir()
-    data = {}
-    data[
-        "usps.bz2"
-    ] = "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/usps.bz2"
-    data[
-        "usps.t.bz2"
-    ] = "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/usps.t.bz2"
-
-    download_all(data, out_dir)
-
-
-def get_SVHN(out_dir: Path):
-    """Get SVHN dataset (Street view house numbers)"""
-    if out_dir.exists():
-        print(f"> Directory ({out_dir}) exists, skipping downloading SVHN.")
-        return
-
-    out_dir.mkdir()
-
-    data = {}
-    data["train_32x32.mat"] = "http://ufldl.stanford.edu/housenumbers/train_32x32.mat"
-    data["test_32x32.mat"] = "http://ufldl.stanford.edu/housenumbers/test_32x32.mat"
-
-    download_all(data, out_dir)
-
-
-def get_MNIST(out_dir: Path):
-    """Downloads MNIST using torchvision routines.
-
-    Then, move processed files to directory expected by
-    `utils/data_processing.py`. Delete the rest.
-    """
-
-    if (out_dir / "MNIST").exists():
-        print(f"> Directory ({out_dir}) exists, skipping downloading MNIST.")
-        print(type(out_dir))
-        return
-
-    datasets.MNIST(out_dir, train=True, download=True)
-
-    datasets.MNIST(out_dir, train=False)
-
-    train_file = "training.pt"
-    test_file = "test.pt"
-    shutil.move(out_dir / "MNIST" / "processed" / train_file, out_dir / "MNIST" / train_file)  # type: ignore[arg-type]
-    shutil.move(out_dir / "MNIST" / "processed" / test_file, out_dir / "MNIST" / test_file)  # type: ignore[arg-type]
-    shutil.rmtree(out_dir / "MNIST" / "raw")
-    shutil.rmtree(out_dir / "MNIST" / "processed")
-
-
-def main():
-    """Get all the datasets."""
-
-    data_dir = Path("./data")
-
-    data_dir.mkdir(exist_ok=True)
-
-    get_MNIST(data_dir)  # type: ignore[arg-type]
-
-    get_SVHN(data_dir / "SVHN")
-
-    get_USPS(data_dir / "USPS")
-
-    get_MNISTM(data_dir / "MNIST_M")
-
-    get_synthDigits(data_dir / "SynthDigits")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_preprocess.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_preprocess.py
deleted file mode 100644
index df2cd3b89b46..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_preprocess.py
+++ /dev/null
@@ -1,278 +0,0 @@
-"""This file is used to download and pre-process all data in Digit-5 dataset.
-
-i.e., splitted data into train&test set  in a stratified way. The
-function to process data into 10 partitions is also provided.
-"""
-
-
-import bz2
-import os
-import pickle as pkl
-from collections import Counter
-
-import numpy as np
-import scipy.io as scio  # type: ignore
-import torch
-from sklearn import model_selection  # type: ignore
-
-# pylint: disable=invalid-name, too-many-locals, broad-except
-
-
-def stratified_split(X, y):
-    """Provides train/test indices to split data in train/test sets."""
-
-    sss = model_selection.StratifiedShuffleSplit(
-        n_splits=1, test_size=0.2, random_state=0
-    )
-
-    for train_index, test_index in sss.split(X, y):
-        X_train, X_test = X[train_index], X[test_index]
-        y_train, y_test = y[train_index], y[test_index]
-        print("Train:", Counter(y_train))
-        print("Test:", Counter(y_test))
-
-    return (X_train, y_train), (X_test, y_test)
-
-
-def process_mnist():
-    """
-    train:
-    (56000, 28, 28)
-    (56000,)
-    test:
-    (14000, 28, 28)
-    (14000,)
-    """
-    mnist_train = "./data/MNIST/training.pt"
-    mnist_test = "./data/MNIST/test.pt"
-    train = torch.load(mnist_train)
-    test = torch.load(mnist_test)
-
-    train_img = train[0].numpy()
-    train_tar = train[1].numpy()
-
-    test_img = test[0].numpy()
-    test_tar = test[1].numpy()
-
-    all_img = np.concatenate([train_img, test_img])
-    all_tar = np.concatenate([train_tar, test_tar])
-
-    train_stratified, test_stratified = stratified_split(all_img, all_tar)
-    print("# After spliting:")
-    print("Train imgs:\t", train_stratified[0].shape)
-    print("Train labels:\t", train_stratified[1].shape)
-    print("Test imgs:\t", test_stratified[0].shape)
-    print("Test labels:\t", test_stratified[1].shape)
-
-    with open("./data/MNIST/train.pkl", "wb") as mnist_train_file:
-        pkl.dump(train_stratified, mnist_train_file, pkl.HIGHEST_PROTOCOL)
-
-    with open("./data/MNIST/test.pkl", "wb") as mnist_test_file:
-        pkl.dump(test_stratified, mnist_test_file, pkl.HIGHEST_PROTOCOL)
-
-
-def process_svhn():
-    """
-    train:
-    (79431, 32, 32, 3)
-    (79431,)
-    test:
-    (19858, 32, 32, 3)
-    (19858,)
-    """
-    train = scio.loadmat("./data/SVHN/train_32x32.mat")
-    test = scio.loadmat("./data/SVHN/test_32x32.mat")
-
-    train_img = train["X"]
-    train_tar = train["y"].astype(np.int64).squeeze()
-
-    test_img = test["X"]
-    test_tar = test["y"].astype(np.int64).squeeze()
-
-    train_img = np.transpose(train_img, (3, 0, 1, 2))
-    test_img = np.transpose(test_img, (3, 0, 1, 2))
-
-    np.place(train_tar, train_tar == 10, 0)
-    np.place(test_tar, test_tar == 10, 0)
-
-    all_img = np.concatenate([train_img, test_img])
-    all_tar = np.concatenate([train_tar, test_tar])
-
-    train_stratified, test_stratified = stratified_split(all_img, all_tar)
-    print("# After spliting:")
-    print("Train imgs:\t", train_stratified[0].shape)
-    print("Train labels:\t", train_stratified[1].shape)
-    print("Test imgs:\t", test_stratified[0].shape)
-    print("Test labels:\t", test_stratified[1].shape)
-
-    with open("./data/SVHN/train.pkl", "wb") as svhn_train_file:
-        pkl.dump(train_stratified, svhn_train_file, pkl.HIGHEST_PROTOCOL)
-
-    with open("./data/SVHN/test.pkl", "wb") as svhn_test_file:
-        pkl.dump(test_stratified, svhn_test_file, pkl.HIGHEST_PROTOCOL)
-
-
-def process_usps():
-    """
-    train:
-    (7438, 16, 16)
-    (7438,)
-    test:
-    (1860, 16, 16)
-    (1860,)
-    :return:
-    """
-
-    train_path = "./data/USPS/usps.bz2"
-    with bz2.open(train_path) as fp:
-        raw_data = [l.decode().split() for l in fp.readlines()]
-    imgs = [[x.split(":")[-1] for x in data[1:]] for data in raw_data]
-    imgs = np.asarray(imgs, dtype=np.float32).reshape((-1, 16, 16))
-    imgs = ((imgs + 1) / 2 * 255).astype(dtype=np.uint8)
-    targets = [int(d[0]) - 1 for d in raw_data]
-
-    train_img = imgs
-    train_tar = np.array(targets)
-
-    test_path = "./data/USPS/usps.t.bz2"
-    with bz2.open(test_path) as fp:
-        raw_data = [l.decode().split() for l in fp.readlines()]
-    imgs = [[x.split(":")[-1] for x in data[1:]] for data in raw_data]
-    imgs = np.asarray(imgs, dtype=np.float32).reshape((-1, 16, 16))
-    imgs = ((imgs + 1) / 2 * 255).astype(dtype=np.uint8)
-    targets = [int(d[0]) - 1 for d in raw_data]
-
-    test_img = imgs
-    test_tar = np.array(targets)
-
-    all_img = np.concatenate([train_img, test_img])
-    all_tar = np.concatenate([train_tar, test_tar])
-
-    train_stratified, test_stratified = stratified_split(all_img, all_tar)
-    print("# After spliting:")
-    print("Train imgs:\t", train_stratified[0].shape)
-    print("Train labels:\t", train_stratified[1].shape)
-    print("Test imgs:\t", test_stratified[0].shape)
-    print("Test labels:\t", test_stratified[1].shape)
-
-    with open("./data/USPS/train.pkl", "wb") as usps_train_file:
-        pkl.dump(train_stratified, usps_train_file, pkl.HIGHEST_PROTOCOL)
-
-    with open("./data/USPS/test.pkl", "wb") as usps_test_file:
-        pkl.dump(test_stratified, usps_test_file, pkl.HIGHEST_PROTOCOL)
-
-
-def process_synth():
-    """(391162, 32, 32, 3) (391162,) (97791, 32, 32, 3) (97791,)"""
-    train = scio.loadmat("./data/SynthDigits/synth_train_32x32.mat")
-    test = scio.loadmat("./data/SynthDigits/synth_test_32x32.mat")
-
-    train_img = train["X"]
-    train_tar = train["y"].astype(np.int64).squeeze()
-
-    test_img = test["X"]
-    test_tar = test["y"].astype(np.int64).squeeze()
-
-    train_img = np.transpose(train_img, (3, 0, 1, 2))
-    test_img = np.transpose(test_img, (3, 0, 1, 2))
-
-    all_img = np.concatenate([train_img, test_img])
-    all_tar = np.concatenate([train_tar, test_tar])
-
-    train_stratified, test_stratified = stratified_split(all_img, all_tar)
-    print("# After spliting:")
-    print("Train imgs:\t", train_stratified[0].shape)
-    print("Train labels:\t", train_stratified[1].shape)
-    print("Test imgs:\t", test_stratified[0].shape)
-    print("Test labels:\t", test_stratified[1].shape)
-
-    with open("./data/SynthDigits/train.pkl", "wb") as synthdigits_train_file:
-        pkl.dump(train_stratified, synthdigits_train_file, pkl.HIGHEST_PROTOCOL)
-
-    with open("./data/SynthDigits/test.pkl", "wb") as synthdigits_test_file:
-        pkl.dump(test_stratified, synthdigits_test_file, pkl.HIGHEST_PROTOCOL)
-
-
-def process_mnistm():
-    """
-    (56000, 28, 28, 3)
-    (56000,)
-    (14000, 28, 28, 3)
-    (14000,)
-    :return:
-    """
-    data = np.load("./data/MNIST_M/mnistm_data.pkl", allow_pickle=True)
-    train_img = data["train"]
-    train_tar = data["train_label"]
-    valid_img = data["valid"]
-    valid_tar = data["valid_label"]
-    test_img = data["test"]
-    test_tar = data["test_label"]
-
-    all_img = np.concatenate([train_img, valid_img, test_img])
-    all_tar = np.concatenate([train_tar, valid_tar, test_tar])
-
-    train_stratified, test_stratified = stratified_split(all_img, all_tar)
-    print("# After spliting:")
-    print("Train imgs:\t", train_stratified[0].shape)
-    print("Train labels:\t", train_stratified[1].shape)
-    print("Test imgs:\t", test_stratified[0].shape)
-    print("Test labels:\t", test_stratified[1].shape)
-
-    with open("./data/MNIST_M/train.pkl", "wb") as mnistm_train_file:
-        pkl.dump(train_stratified, mnistm_train_file, pkl.HIGHEST_PROTOCOL)
-
-    with open("./data/MNIST_M/test.pkl", "wb") as mnistm_test_file:
-        pkl.dump(test_stratified, mnistm_test_file, pkl.HIGHEST_PROTOCOL)
-
-
-def split(data_path, percentage=0.1):
-    """split each single dataset into multiple partitions for client scaling
-    training each part remain the same size according to the smallest datasize
-    (i.e. 743)"""
-    images, labels = np.load(os.path.join(data_path, "train.pkl"), allow_pickle=True)
-    part_len = 743.8
-    part_num = int(1.0 / percentage)
-
-    for num in range(part_num):
-        images_part = images[int(part_len * num) : int(part_len * (num + 1)), :, :]
-        labels_part = labels[int(part_len * num) : int(part_len * (num + 1))]
-
-        save_path = os.path.join(data_path, "partitions")
-        if not os.path.exists(save_path):
-            os.makedirs(save_path)
-        with open(
-            os.path.join(save_path, "train_part{}.pkl".format(num)), "wb"
-        ) as file_split:
-            pkl.dump((images_part, labels_part), file_split, pkl.HIGHEST_PROTOCOL)
-
-
-if __name__ == "__main__":
-    print("Processing...")
-    print("--------MNIST---------")
-    process_mnist()
-    print("--------SVHN---------")
-    process_svhn()
-    print("--------USPS---------")
-    process_usps()
-    print("--------MNIST-M---------")
-    process_mnistm()
-    print("-------SynthDigits-------")
-    try:
-        process_synth()
-    except Exception as exception:
-        print(f"unable to process SynthDigits: {exception}")
-
-    base_paths = [
-        "./data/MNIST",
-        "./data/SVHN",
-        "./data/USPS",
-        "./data/MNIST_M",
-        "./data/SynthDigits",
-    ]
-    for path in base_paths:
-        print(f"Spliting {os.path.basename(path)}")
-        try:
-            split(path)
-        except Exception as exception:
-            print(f"Failed to split: {path} --> {exception}")
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_utils.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_utils.py
deleted file mode 100644
index c465e3ea10d1..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/data_utils.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""This code creates 10 different partitions of each datasets."""
-
-
-import os
-import sys
-
-import numpy as np
-from PIL import Image  # type: ignore
-from torch.utils.data import Dataset
-
-base_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(base_path)
-
-
-class DigitsDataset(Dataset):
-    """Split datasets."""
-
-    # pylint: disable=too-many-arguments
-    def __init__(
-        self,
-        data_path,
-        channels,
-        percent=0.1,
-        filename=None,
-        train=True,
-        transform=None,
-    ):
-        if filename is None:
-            if train:
-                if percent >= 0.1:
-                    for part in range(int(percent * 10)):
-                        if part == 0:
-                            self.images, self.labels = np.load(
-                                os.path.join(
-                                    data_path,
-                                    f"partitions/train_part{part}.pkl",
-                                ),
-                                allow_pickle=True,
-                            )
-                        else:
-                            images, labels = np.load(
-                                os.path.join(
-                                    data_path,
-                                    f"partitions/train_part{part}.pkl",
-                                ),
-                                allow_pickle=True,
-                            )
-                            self.images = np.concatenate([self.images, images], axis=0)
-                            self.labels = np.concatenate([self.labels, labels], axis=0)
-                else:
-                    self.images, self.labels = np.load(
-                        os.path.join(data_path, "partitions/train_part0.pkl"),
-                        allow_pickle=True,
-                    )
-                    data_len = int(self.images.shape[0] * percent * 10)
-                    self.images = self.images[:data_len]
-                    self.labels = self.labels[:data_len]
-            else:
-                self.images, self.labels = np.load(
-                    os.path.join(data_path, "test.pkl"), allow_pickle=True
-                )
-        else:
-            self.images, self.labels = np.load(
-                os.path.join(data_path, filename), allow_pickle=True
-            )
-
-        self.transform = transform
-        self.channels = channels
-        self.labels = self.labels.astype(np.long).squeeze()
-
-    def __len__(self):
-        return self.images.shape[0]
-
-    def __getitem__(self, idx):
-        image = self.images[idx]
-        label = self.labels[idx]
-        if self.channels == 1:
-            image = Image.fromarray(image, mode="L")
-        elif self.channels == 3:
-            image = Image.fromarray(image, mode="RGB")
-        else:
-            raise ValueError(f"{self.channels} channel is not allowed.")
-
-        if self.transform is not None:
-            image = self.transform(image)
-
-        return image, label
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/mnistm/__init__.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/mnistm/__init__.py
deleted file mode 100644
index e69de29bb2d1..000000000000
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/mnistm/create_mnistm.py b/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/mnistm/create_mnistm.py
deleted file mode 100644
index 5af23d52122a..000000000000
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedbn/convergence_rate/utils/mnistm/create_mnistm.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""! This script has been borrowed and adapted. Original script:
-https://github.com/pumpikano/tf-dann/blob/master/create_mnistm.py.
-
-It creatse the MNIST-M dataset based on MNIST
-"""
-
-
-import tarfile
-from typing import Any
-
-import numpy as np
-import skimage  # type: ignore
-import skimage.io  # type: ignore
-import skimage.transform  # type: ignore
-
-
-# pylint: disable=invalid-name, disable=no-member, bare-except
-def compose_image(digit: Any, background: Any) -> Any:
-    """Difference-blend a digit and a random patch from a background image."""
-    w, h, _ = background.shape
-    dw, dh, _ = digit.shape
-    x = np.random.randint(0, w - dw)
-    y = np.random.randint(0, h - dh)
-
-    bg = background[x : x + dw, y : y + dh]
-    return np.abs(bg - digit).astype(np.uint8)
-
-
-def mnist_to_img(x: Any) -> Any:
-    """Binarize MNIST digit and convert to RGB."""
-    x = (x > 0).float()
-    d = x.reshape([28, 28, 1]) * 255
-    return np.concatenate([d, d, d], 2)
-
-
-def create_mnistm(X: Any) -> Any:
-    """Give an array of MNIST digits, blend random background patches to build
-    the MNIST-M dataset as described in
-    http://jmlr.org/papers/volume17/15-239/15-239.pdf."""
-
-    bst_path = "./data/MNIST_M/BSR_bsds500.tgz"
-
-    rand = np.random.RandomState(42)
-    train_files = []
-
-    with tarfile.open(bst_path, "r") as bsr_file:
-        for name in bsr_file.getnames():
-            if name.startswith("BSR/BSDS500/data/images/train/"):
-                train_files.append(name)
-
-        print("Loading BSR training images")
-        background_data = []
-        for name in train_files:
-            try:
-                fp = bsr_file.extractfile(name)
-                bg_img = skimage.io.imread(fp)
-                background_data.append(bg_img)
-            except:
-                continue
-
-    X_ = np.zeros([X.shape[0], 28, 28, 3], np.uint8)
-    for i in range(X.shape[0]):
-        if i % 1000 == 0:
-            print("Processing example", i)
-
-        bg_img = rand.choice(background_data)
-        d = mnist_to_img(X[i])
-        d = compose_image(d, bg_img)
-        X_[i] = d
-
-    return X_

From c3cf430ec4c2a0140dbfc4f9ebe39f35faf61ca6 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 16 Nov 2023 16:41:33 +0100
Subject: [PATCH 20/64] Implement Bulyan (#1817) (#1891)

Co-authored-by: Daniel J. Beutel <daniel@adap.com>
Co-authored-by: Edoardo Gabrielli <edo.gab33@gmail.com>
Co-authored-by: jafermarq <javier@flower.dev>
---
 doc/source/ref-api-flwr.rst                   |  10 +
 doc/source/ref-changelog.md                   |   4 +
 src/py/flwr/server/strategy/__init__.py       |   2 +
 src/py/flwr/server/strategy/aggregate.py      | 176 +++++++++++++++++-
 src/py/flwr/server/strategy/aggregate_test.py |  77 +++++++-
 src/py/flwr/server/strategy/bulyan.py         | 162 ++++++++++++++++
 src/py/flwr/server/strategy/bulyan_test.py    | 131 +++++++++++++
 7 files changed, 558 insertions(+), 4 deletions(-)
 create mode 100644 src/py/flwr/server/strategy/bulyan.py
 create mode 100644 src/py/flwr/server/strategy/bulyan_test.py

diff --git a/doc/source/ref-api-flwr.rst b/doc/source/ref-api-flwr.rst
index 07936f117444..e1983cd92c90 100644
--- a/doc/source/ref-api-flwr.rst
+++ b/doc/source/ref-api-flwr.rst
@@ -214,6 +214,16 @@ server.strategy.Krum
 
     .. automethod:: __init__
 
+.. _flwr-server-strategy-Bulyan-apiref:
+
+server.strategy.Bulyan
+^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: flwr.server.strategy.Bulyan
+    :members:
+
+    .. automethod:: __init__
+
 
 .. _flwr-server-strategy-FedXgbNnAvg-apiref:
 
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 299228170b80..10112088b88c 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -58,6 +58,10 @@
 
   Flower received many improvements under the hood, too many to list here.
 
+- **Add new** `Bulyan` **strategy** ([#1817](https://github.com/adap/flower/pull/1817), [#1891](https://github.com/adap/flower/pull/1891))
+
+  The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., 2018](https://arxiv.org/abs/1802.07927)
+
 ### Incompatible changes
 
 - **Remove support for Python 3.7** ([#2280](https://github.com/adap/flower/pull/2280), [#2299](https://github.com/adap/flower/pull/2299), [#2304](https://github.com/adap/flower/pull/2304), [#2306](https://github.com/adap/flower/pull/2306), [#2355](https://github.com/adap/flower/pull/2355), [#2356](https://github.com/adap/flower/pull/2356))
diff --git a/src/py/flwr/server/strategy/__init__.py b/src/py/flwr/server/strategy/__init__.py
index 72429694bfe7..908267d04b3f 100644
--- a/src/py/flwr/server/strategy/__init__.py
+++ b/src/py/flwr/server/strategy/__init__.py
@@ -15,6 +15,7 @@
 """Contains the strategy abstraction and different implementations."""
 
 
+from .bulyan import Bulyan as Bulyan
 from .dpfedavg_adaptive import DPFedAvgAdaptive as DPFedAvgAdaptive
 from .dpfedavg_fixed import DPFedAvgFixed as DPFedAvgFixed
 from .fault_tolerant_fedavg import FaultTolerantFedAvg as FaultTolerantFedAvg
@@ -48,6 +49,7 @@
     "FedMedian",
     "FedTrimmedAvg",
     "Krum",
+    "Bulyan",
     "DPFedAvgAdaptive",
     "DPFedAvgFixed",
     "Strategy",
diff --git a/src/py/flwr/server/strategy/aggregate.py b/src/py/flwr/server/strategy/aggregate.py
index 42390a08a110..63926f2eaa51 100644
--- a/src/py/flwr/server/strategy/aggregate.py
+++ b/src/py/flwr/server/strategy/aggregate.py
@@ -13,10 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 """Aggregation functions for strategy implementations."""
-
+# mypy: disallow_untyped_calls=False
 
 from functools import reduce
-from typing import List, Tuple
+from typing import Any, Callable, List, Tuple
 
 import numpy as np
 
@@ -56,7 +56,7 @@ def aggregate_median(results: List[Tuple[NDArrays, int]]) -> NDArrays:
 def aggregate_krum(
     results: List[Tuple[NDArrays, int]], num_malicious: int, to_keep: int
 ) -> NDArrays:
-    """Choose one parameter vector according to the Krum fucntion.
+    """Choose one parameter vector according to the Krum function.
 
     If to_keep is not None, then MultiKrum is applied.
     """
@@ -91,6 +91,89 @@ def aggregate_krum(
     return weights[np.argmin(scores)]
 
 
+# pylint: disable=too-many-locals
+def aggregate_bulyan(
+    results: List[Tuple[NDArrays, int]],
+    num_malicious: int,
+    aggregation_rule: Callable,  # type: ignore
+    **aggregation_rule_kwargs: Any,
+) -> NDArrays:
+    """Perform Bulyan aggregation.
+
+    Parameters
+    ----------
+    results: List[Tuple[NDArrays, int]]
+        Weights and number of samples for each of the client.
+    num_malicious: int
+        The maximum number of malicious clients.
+    aggregation_rule: Callable
+        Byzantine resilient aggregation rule used as the first step of the Bulyan
+    aggregation_rule_kwargs: Any
+        The arguments to the aggregation rule.
+
+    Returns
+    -------
+    aggregated_parameters: NDArrays
+        Aggregated parameters according to the Bulyan strategy.
+    """
+    byzantine_resilient_single_ret_model_aggregation = [aggregate_krum]
+    # also GeoMed (but not implemented yet)
+    byzantine_resilient_many_return_models_aggregation = []  # type: ignore
+    # Brute, Medoid (but not implemented yet)
+
+    num_clients = len(results)
+    if num_clients < 4 * num_malicious + 3:
+        raise ValueError(
+            "The Bulyan aggregation requires then number of clients to be greater or "
+            "equal to the 4 * num_malicious + 3. This is the assumption of this method."
+            "It is needed to ensure that the method reduces the attacker's leeway to "
+            "the one proved in the paper."
+        )
+    selected_models_set: List[Tuple[NDArrays, int]] = []
+
+    theta = len(results) - 2 * num_malicious
+    beta = theta - 2 * num_malicious
+
+    for _ in range(theta):
+        best_model = aggregation_rule(
+            results=results, num_malicious=num_malicious, **aggregation_rule_kwargs
+        )
+        list_of_weights = [weights for weights, num_samples in results]
+        # This group gives exact result
+        if aggregation_rule in byzantine_resilient_single_ret_model_aggregation:
+            best_idx = _find_reference_weights(best_model, list_of_weights)
+        # This group requires finding the closest model to the returned one
+        # (weights distance wise)
+        elif aggregation_rule in byzantine_resilient_many_return_models_aggregation:
+            # when different aggregation strategies available
+            # write a function to find the closest model
+            raise NotImplementedError(
+                "aggregate_bulyan currently does not support the aggregation rules that"
+                " return many models as results. "
+                "Such aggregation rules are currently not available in Flower."
+            )
+        else:
+            raise ValueError(
+                "The given aggregation rule is not added as Byzantine resilient. "
+                "Please choose from Byzantine resilient rules."
+            )
+
+        selected_models_set.append(results[best_idx])
+
+        # remove idx from tracker and weights_results
+        results.pop(best_idx)
+
+    # Compute median parameter vector across selected_models_set
+    median_vect = aggregate_median(selected_models_set)
+
+    # Take the averaged beta parameters of the closest distance to the median
+    # (coordinate-wise)
+    parameters_aggregated = _aggregate_n_closest_weights(
+        median_vect, selected_models_set, beta_closest=beta
+    )
+    return parameters_aggregated
+
+
 def weighted_loss_avg(results: List[Tuple[int, float]]) -> float:
     """Aggregate evaluation results obtained from multiple clients."""
     num_total_evaluation_examples = sum([num_examples for num_examples, _ in results])
@@ -168,3 +251,90 @@ def aggregate_trimmed_avg(
     ]
 
     return trimmed_w
+
+
+def _check_weights_equality(weights1: NDArrays, weights2: NDArrays) -> bool:
+    """Check if weights are the same."""
+    if len(weights1) != len(weights2):
+        return False
+    return all(
+        np.array_equal(layer_weights1, layer_weights2)
+        for layer_weights1, layer_weights2 in zip(weights1, weights2)
+    )
+
+
+def _find_reference_weights(
+    reference_weights: NDArrays, list_of_weights: List[NDArrays]
+) -> int:
+    """Find the reference weights by looping through the `list_of_weights`.
+
+    Raise Error if the reference weights is not found.
+
+    Parameters
+    ----------
+    reference_weights: NDArrays
+        Weights that will be searched for.
+    list_of_weights: List[NDArrays]
+        List of weights that will be searched through.
+
+    Returns
+    -------
+    index: int
+        The index of `reference_weights` in the `list_of_weights`.
+
+    Raises
+    ------
+    ValueError
+        If `reference_weights` is not found in `list_of_weights`.
+    """
+    for idx, weights in enumerate(list_of_weights):
+        if _check_weights_equality(reference_weights, weights):
+            return idx
+    raise ValueError("The reference weights not found in list_of_weights.")
+
+
+def _aggregate_n_closest_weights(
+    reference_weights: NDArrays, results: List[Tuple[NDArrays, int]], beta_closest: int
+) -> NDArrays:
+    """Calculate element-wise mean of the `N` closest values.
+
+    Note, each i-th coordinate of the result weight is the average of the beta_closest
+    -ith coordinates to the reference weights
+
+
+    Parameters
+    ----------
+    reference_weights: NDArrays
+        The weights from which the distances will be computed
+    results: List[Tuple[NDArrays, int]]
+        The weights from models
+    beta_closest: int
+        The number of the closest distance weights that will be averaged
+
+    Returns
+    -------
+    aggregated_weights: NDArrays
+        Averaged (element-wise) beta weights that have the closest distance to
+         reference weights
+    """
+    list_of_weights = [weights for weights, num_examples in results]
+    aggregated_weights = []
+
+    for layer_id, layer_weights in enumerate(reference_weights):
+        other_weights_layer_list = []
+        for other_w in list_of_weights:
+            other_weights_layer = other_w[layer_id]
+            other_weights_layer_list.append(other_weights_layer)
+        other_weights_layer_np = np.array(other_weights_layer_list)
+        diff_np = np.abs(layer_weights - other_weights_layer_np)
+        # Create indices of the smallest differences
+        # We do not need the exact order but just the beta closest weights
+        # therefore np.argpartition is used instead of np.argsort
+        indices = np.argpartition(diff_np, kth=beta_closest - 1, axis=0)
+        # Take the weights (coordinate-wise) corresponding to the beta of the
+        # closest distances
+        beta_closest_weights = np.take_along_axis(
+            other_weights_layer_np, indices=indices, axis=0
+        )[:beta_closest]
+        aggregated_weights.append(np.mean(beta_closest_weights, axis=0))
+    return aggregated_weights
diff --git a/src/py/flwr/server/strategy/aggregate_test.py b/src/py/flwr/server/strategy/aggregate_test.py
index 81cc7189b14d..f8b4e3c03b50 100644
--- a/src/py/flwr/server/strategy/aggregate_test.py
+++ b/src/py/flwr/server/strategy/aggregate_test.py
@@ -19,7 +19,13 @@
 
 import numpy as np
 
-from .aggregate import aggregate, weighted_loss_avg
+from .aggregate import (
+    _aggregate_n_closest_weights,
+    _check_weights_equality,
+    _find_reference_weights,
+    aggregate,
+    weighted_loss_avg,
+)
 
 
 def test_aggregate() -> None:
@@ -64,3 +70,72 @@ def test_weighted_loss_avg_multiple_values() -> None:
 
     # Assert
     assert expected == actual
+
+
+def test_check_weights_equality_true() -> None:
+    """Check weights equality - the same weights."""
+    weights1 = [np.array([1, 2]), np.array([[1, 2], [3, 4]])]
+    weights2 = [np.array([1, 2]), np.array([[1, 2], [3, 4]])]
+    results = _check_weights_equality(weights1, weights2)
+    expected = True
+    assert expected == results
+
+
+def test_check_weights_equality_numeric_false() -> None:
+    """Check weights equality - different weights, same length."""
+    weights1 = [np.array([1, 2]), np.array([[1, 2], [3, 4]])]
+    weights2 = [np.array([2, 2]), np.array([[1, 2], [3, 4]])]
+    results = _check_weights_equality(weights1, weights2)
+    expected = False
+    assert expected == results
+
+
+def test_check_weights_equality_various_length_false() -> None:
+    """Check weights equality - the same first layer weights, different length."""
+    weights1 = [np.array([1, 2]), np.array([[1, 2], [3, 4]])]
+    weights2 = [np.array([1, 2])]
+    results = _check_weights_equality(weights1, weights2)
+    expected = False
+    assert expected == results
+
+
+def test_find_reference_weights() -> None:
+    """Check if the finding weights from list of weigths work."""
+    reference_weights = [np.array([1, 2]), np.array([[1, 2], [3, 4]])]
+    list_of_weights = [
+        [np.array([2, 2]), np.array([[1, 2], [3, 4]])],
+        [np.array([3, 2]), np.array([[1, 2], [3, 4]])],
+        [np.array([3, 2]), np.array([[1, 2], [10, 4]])],
+        [np.array([1, 2]), np.array([[1, 2], [3, 4]])],
+    ]
+
+    result = _find_reference_weights(reference_weights, list_of_weights)
+
+    expected = 3
+    assert result == expected
+
+
+def test_aggregate_n_closest_weights_mean() -> None:
+    """Check if aggregation of n closest weights to the reference works."""
+    beta_closest = 2
+    reference_weights = [np.array([1, 2]), np.array([[1, 2], [3, 4]])]
+
+    list_of_weights = [
+        [np.array([1, 2]), np.array([[1, 2], [3, 4]])],
+        [np.array([1.1, 2.1]), np.array([[1.1, 2.1], [3.1, 4.1]])],
+        [np.array([1.2, 2.2]), np.array([[1.2, 2.2], [3.2, 4.2]])],
+        [np.array([1.3, 2.3]), np.array([[0.9, 2.5], [3.4, 3.8]])],
+    ]
+    list_of_weights_and_samples = [(weights, 0) for weights in list_of_weights]
+
+    beta_closest_weights = _aggregate_n_closest_weights(
+        reference_weights, list_of_weights_and_samples, beta_closest=beta_closest
+    )
+    expected_averaged = [np.array([1.05, 2.05]), np.array([[0.95, 2.05], [3.05, 4.05]])]
+
+    assert all(
+        (
+            np.array_equal(expected, result)
+            for expected, result in zip(expected_averaged, beta_closest_weights)
+        )
+    )
diff --git a/src/py/flwr/server/strategy/bulyan.py b/src/py/flwr/server/strategy/bulyan.py
new file mode 100644
index 000000000000..0243f4e6546f
--- /dev/null
+++ b/src/py/flwr/server/strategy/bulyan.py
@@ -0,0 +1,162 @@
+# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Bulyan [El Mhamdi et al., 2018] strategy.
+
+Paper: arxiv.org/abs/1802.07927
+"""
+
+
+from logging import WARNING
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from flwr.common import (
+    FitRes,
+    MetricsAggregationFn,
+    NDArrays,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.common.logger import log
+from flwr.server.client_proxy import ClientProxy
+
+from .aggregate import aggregate_bulyan, aggregate_krum
+from .fedavg import FedAvg
+
+
+# flake8: noqa: E501
+class Bulyan(FedAvg):
+    """Bulyan strategy implementation."""
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes,line-too-long, too-many-locals
+    def __init__(
+        self,
+        *,
+        fraction_fit: float = 1.0,
+        fraction_evaluate: float = 1.0,
+        min_fit_clients: int = 2,
+        min_evaluate_clients: int = 2,
+        min_available_clients: int = 2,
+        num_malicious_clients: int = 0,
+        evaluate_fn: Optional[
+            Callable[
+                [int, NDArrays, Dict[str, Scalar]],
+                Optional[Tuple[float, Dict[str, Scalar]]],
+            ]
+        ] = None,
+        on_fit_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
+        on_evaluate_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
+        accept_failures: bool = True,
+        initial_parameters: Optional[Parameters] = None,
+        fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
+        evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
+        first_aggregation_rule: Callable = aggregate_krum,  # type: ignore
+        **aggregation_rule_kwargs: Any,
+    ) -> None:
+        """Bulyan strategy.
+
+        Implementation based on https://arxiv.org/abs/1802.07927.
+
+        Parameters
+        ----------
+        fraction_fit : float, optional
+            Fraction of clients used during training. Defaults to 1.0.
+        fraction_evaluate : float, optional
+            Fraction of clients used during validation. Defaults to 1.0.
+        min_fit_clients : int, optional
+            Minimum number of clients used during training. Defaults to 2.
+        min_evaluate_clients : int, optional
+            Minimum number of clients used during validation. Defaults to 2.
+        min_available_clients : int, optional
+            Minimum number of total clients in the system. Defaults to 2.
+        num_malicious_clients : int, optional
+            Number of malicious clients in the system. Defaults to 0.
+        evaluate_fn : Optional[Callable[[int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]]]
+            Optional function used for validation. Defaults to None.
+        on_fit_config_fn : Callable[[int], Dict[str, Scalar]], optional
+            Function used to configure training. Defaults to None.
+        on_evaluate_config_fn : Callable[[int], Dict[str, Scalar]], optional
+            Function used to configure validation. Defaults to None.
+        accept_failures : bool, optional
+            Whether or not accept rounds containing failures. Defaults to True.
+        initial_parameters : Parameters, optional
+            Initial global model parameters.
+        first_aggregation_rule: Callable
+            Byzantine resilient aggregation rule that is used as the first step of the Bulyan (e.g., Krum)
+        **aggregation_rule_kwargs: Any
+            arguments to the first_aggregation rule
+        """
+        super().__init__(
+            fraction_fit=fraction_fit,
+            fraction_evaluate=fraction_evaluate,
+            min_fit_clients=min_fit_clients,
+            min_evaluate_clients=min_evaluate_clients,
+            min_available_clients=min_available_clients,
+            evaluate_fn=evaluate_fn,
+            on_fit_config_fn=on_fit_config_fn,
+            on_evaluate_config_fn=on_evaluate_config_fn,
+            accept_failures=accept_failures,
+            initial_parameters=initial_parameters,
+            fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,
+            evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn,
+        )
+        self.num_malicious_clients = num_malicious_clients
+        self.first_aggregation_rule = first_aggregation_rule
+        self.aggregation_rule_kwargs = aggregation_rule_kwargs
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = f"Bulyan(accept_failures={self.accept_failures})"
+        return rep
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate fit results using Bulyan."""
+        if not results:
+            return None, {}
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Convert results
+        weights_results = [
+            (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
+            for _, fit_res in results
+        ]
+
+        # Aggregate weights
+        parameters_aggregated = ndarrays_to_parameters(
+            aggregate_bulyan(
+                weights_results,
+                self.num_malicious_clients,
+                self.first_aggregation_rule,
+                **self.aggregation_rule_kwargs,
+            )
+        )
+
+        # Aggregate custom metrics if aggregation fn was provided
+        metrics_aggregated = {}
+        if self.fit_metrics_aggregation_fn:
+            fit_metrics = [(res.num_examples, res.metrics) for _, res in results]
+            metrics_aggregated = self.fit_metrics_aggregation_fn(fit_metrics)
+        elif server_round == 1:  # Only log this warning once
+            log(WARNING, "No fit_metrics_aggregation_fn provided")
+
+        return parameters_aggregated, metrics_aggregated
diff --git a/src/py/flwr/server/strategy/bulyan_test.py b/src/py/flwr/server/strategy/bulyan_test.py
new file mode 100644
index 000000000000..299ed49066fb
--- /dev/null
+++ b/src/py/flwr/server/strategy/bulyan_test.py
@@ -0,0 +1,131 @@
+# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Bulyan tests."""
+
+
+from typing import List, Tuple
+from unittest.mock import MagicMock
+
+from numpy import array, float32
+
+from flwr.common import (
+    Code,
+    FitRes,
+    NDArrays,
+    Parameters,
+    Status,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.server.client_proxy import ClientProxy
+
+from .bulyan import Bulyan
+
+
+# pylint: disable=too-many-locals
+def test_aggregate_fit() -> None:
+    """Tests if Bulyan is aggregating correctly."""
+    # Prepare
+    previous_weights: NDArrays = [array([0.1, 0.1, 0.1, 0.1], dtype=float32)]
+    strategy = Bulyan(
+        initial_parameters=ndarrays_to_parameters(previous_weights),
+        num_malicious_clients=0,
+        to_keep=0,
+    )
+    param_0: Parameters = ndarrays_to_parameters(
+        [array([0.2, 0.2, 0.2, 0.2], dtype=float32)]
+    )
+    param_1: Parameters = ndarrays_to_parameters(
+        [array([0.5, 0.5, 0.5, 0.5], dtype=float32)]
+    )
+    param_2: Parameters = ndarrays_to_parameters(
+        [array([0.7, 0.7, 0.7, 0.7], dtype=float32)]
+    )
+    param_3: Parameters = ndarrays_to_parameters(
+        [array([12.0, 12.0, 12.0, 12.0], dtype=float32)]
+    )
+    param_4: Parameters = ndarrays_to_parameters(
+        [array([0.1, 0.1, 0.1, 0.1], dtype=float32)]
+    )
+    param_5: Parameters = ndarrays_to_parameters(
+        [array([0.1, 0.1, 0.1, 0.1], dtype=float32)]
+    )
+    results: List[Tuple[ClientProxy, FitRes]] = [
+        (
+            MagicMock(),
+            FitRes(
+                status=Status(code=Code.OK, message="Success"),
+                parameters=param_0,
+                num_examples=5,
+                metrics={},
+            ),
+        ),
+        (
+            MagicMock(),
+            FitRes(
+                status=Status(code=Code.OK, message="Success"),
+                parameters=param_1,
+                num_examples=5,
+                metrics={},
+            ),
+        ),
+        (
+            MagicMock(),
+            FitRes(
+                status=Status(code=Code.OK, message="Success"),
+                parameters=param_2,
+                num_examples=5,
+                metrics={},
+            ),
+        ),
+        (
+            MagicMock(),
+            FitRes(
+                status=Status(code=Code.OK, message="Success"),
+                parameters=param_3,
+                num_examples=5,
+                metrics={},
+            ),
+        ),
+        (
+            MagicMock(),
+            FitRes(
+                status=Status(code=Code.OK, message="Success"),
+                parameters=param_4,
+                num_examples=5,
+                metrics={},
+            ),
+        ),
+        (
+            MagicMock(),
+            FitRes(
+                status=Status(code=Code.OK, message="Success"),
+                parameters=param_5,
+                num_examples=5,
+                metrics={},
+            ),
+        ),
+    ]
+    coordinate = (0.2 + 0.5 + 0.7 + 12.0 + 0.1 + 0.1) / 6
+    expected: NDArrays = [array([coordinate] * 4, dtype=float32)]
+
+    # Execute
+    actual_aggregated, _ = strategy.aggregate_fit(
+        server_round=1, results=results, failures=[]
+    )
+    if actual_aggregated:
+        actual_list = parameters_to_ndarrays(actual_aggregated)
+        actual = actual_list[0]
+    assert (actual == expected[0]).all()

From 3b54412fff5e2776d2c7229bf700f850b49455b4 Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Fri, 17 Nov 2023 09:32:14 +0000
Subject: [PATCH 21/64] Xgboost-comprehensive: move xgb_bagging strategy to
 core framework and update readme (#2611)

Co-authored-by: yan-gao-GY <yan@flower.dev>
Co-authored-by: Heng Pan <134433891+panh99@users.noreply.github.com>
---
 examples/xgboost-comprehensive/README.md      |   8 +-
 examples/xgboost-comprehensive/dataset.py     |   2 +-
 examples/xgboost-comprehensive/server.py      |   2 +-
 src/py/flwr/server/strategy/__init__.py       |   2 +
 .../py/flwr/server/strategy/fedxgb_bagging.py | 115 +++++++++++-------
 5 files changed, 78 insertions(+), 51 deletions(-)
 rename examples/xgboost-comprehensive/strategy.py => src/py/flwr/server/strategy/fedxgb_bagging.py (52%)

diff --git a/examples/xgboost-comprehensive/README.md b/examples/xgboost-comprehensive/README.md
index 3801d4813a26..3b31e23cb321 100644
--- a/examples/xgboost-comprehensive/README.md
+++ b/examples/xgboost-comprehensive/README.md
@@ -8,17 +8,17 @@ Tree-based with bagging method is used for aggregation on the server.
 Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you:
 
 ```shell
-git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/quickstart-xgboost . && rm -rf flower && cd quickstart-xgboost
+git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/xgboost-comprehensive . && rm -rf flower && cd xgboost-comprehensive
 ```
 
-This will create a new directory called `quickstart-xgboost` containing the following files:
+This will create a new directory called `xgboost-comprehensive` containing the following files:
 
 ```
 -- README.md         <- Your're reading this right now
 -- server.py         <- Defines the server-side logic
--- strategy.py       <- Defines the tree-based bagging aggregation
 -- client.py         <- Defines the client-side logic
 -- dataset.py        <- Defines the functions of data loading and partitioning
+-- utils.py          <- Defines the arguments parser for clients and server.
 -- pyproject.toml    <- Example dependencies (if you use Poetry)
 -- requirements.txt  <- Example dependencies
 ```
@@ -83,5 +83,5 @@ bash run.sh
 ```
 
 Besides, we provide options to customise the experimental settings, including data partitioning and centralised/distributed evaluation (see `utils.py`).
-Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-xgboost)
+Look at the [code](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive)
 and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
diff --git a/examples/xgboost-comprehensive/dataset.py b/examples/xgboost-comprehensive/dataset.py
index 80c978f1077b..bcf2e00b30af 100644
--- a/examples/xgboost-comprehensive/dataset.py
+++ b/examples/xgboost-comprehensive/dataset.py
@@ -1,5 +1,5 @@
 import xgboost as xgb
-from typing import Callable, Dict, List, Optional, Tuple, Union
+from typing import Union
 from datasets import Dataset, DatasetDict, concatenate_datasets
 from flwr_datasets.partitioner import (
     IidPartitioner,
diff --git a/examples/xgboost-comprehensive/server.py b/examples/xgboost-comprehensive/server.py
index 857e99528013..e4c597ee17eb 100644
--- a/examples/xgboost-comprehensive/server.py
+++ b/examples/xgboost-comprehensive/server.py
@@ -6,8 +6,8 @@
 from flwr.common.logger import log
 from flwr.common import Parameters, Scalar
 from flwr_datasets import FederatedDataset
+from flwr.server.strategy import FedXgbBagging
 
-from strategy import FedXgbBagging
 from utils import server_args_parser
 from dataset import resplit, transform_dataset_to_dmatrix
 
diff --git a/src/py/flwr/server/strategy/__init__.py b/src/py/flwr/server/strategy/__init__.py
index 908267d04b3f..0772aa1ff13a 100644
--- a/src/py/flwr/server/strategy/__init__.py
+++ b/src/py/flwr/server/strategy/__init__.py
@@ -28,6 +28,7 @@
 from .fedopt import FedOpt as FedOpt
 from .fedprox import FedProx as FedProx
 from .fedtrimmedavg import FedTrimmedAvg as FedTrimmedAvg
+from .fedxgb_bagging import FedXgbBagging as FedXgbBagging
 from .fedxgb_nn_avg import FedXgbNnAvg as FedXgbNnAvg
 from .fedyogi import FedYogi as FedYogi
 from .krum import Krum as Krum
@@ -40,6 +41,7 @@
     "FedAdam",
     "FedAvg",
     "FedXgbNnAvg",
+    "FedXgbBagging",
     "FedAvgAndroid",
     "FedAvgM",
     "FedOpt",
diff --git a/examples/xgboost-comprehensive/strategy.py b/src/py/flwr/server/strategy/fedxgb_bagging.py
similarity index 52%
rename from examples/xgboost-comprehensive/strategy.py
rename to src/py/flwr/server/strategy/fedxgb_bagging.py
index 814010720a77..cafb466c2e8b 100644
--- a/examples/xgboost-comprehensive/strategy.py
+++ b/src/py/flwr/server/strategy/fedxgb_bagging.py
@@ -1,19 +1,35 @@
-from logging import WARNING
-from typing import Callable, Dict, List, Optional, Tuple, Union
-import flwr as fl
+# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Federated XGBoost bagging aggregation strategy."""
+
+
 import json
+from logging import WARNING
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
 
-from flwr.common import (
-    EvaluateRes,
-    FitRes,
-    Parameters,
-    Scalar,
-)
-from flwr.server.client_proxy import ClientProxy
+from flwr.common import EvaluateRes, FitRes, Parameters, Scalar
 from flwr.common.logger import log
+from flwr.server.client_proxy import ClientProxy
+
+from .fedavg import FedAvg
 
 
-class FedXgbBagging(fl.server.strategy.FedAvg):
+class FedXgbBagging(FedAvg):
+    """Configurable FedXgbBagging strategy implementation."""
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long
     def __init__(
         self,
         evaluate_function: Optional[
@@ -22,10 +38,10 @@ def __init__(
                 Optional[Tuple[float, Dict[str, Scalar]]],
             ]
         ] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         self.evaluate_function = evaluate_function
-        self.global_model = None
+        self.global_model: Optional[bytes] = None
         super().__init__(**kwargs)
 
     def aggregate_fit(
@@ -42,17 +58,16 @@ def aggregate_fit(
             return None, {}
 
         # Aggregate all the client trees
+        global_model = self.global_model
         for _, fit_res in results:
             update = fit_res.parameters.tensors
-            for item in update:
-                self.global_model = aggregate(
-                    self.global_model, json.loads(bytearray(item))
-                )
+            for bst in update:
+                global_model = aggregate(global_model, bst)
 
-        weights_avg = json.dumps(self.global_model)
+        self.global_model = global_model
 
         return (
-            Parameters(tensor_type="", tensors=[bytes(weights_avg, "utf-8")]),
+            Parameters(tensor_type="", tensors=[cast(bytes, global_model)]),
             {},
         )
 
@@ -93,37 +108,47 @@ def evaluate(
         return loss, metrics
 
 
-def aggregate(bst_prev: Optional[Dict], bst_curr: Dict) -> Dict:
+def aggregate(
+    bst_prev_org: Optional[bytes],
+    bst_curr_org: bytes,
+) -> bytes:
     """Conduct bagging aggregation for given trees."""
-    if not bst_prev:
-        return bst_curr
-    else:
-        # Get the tree numbers
-        tree_num_prev, paral_tree_num_prev = _get_tree_nums(bst_prev)
-        tree_num_curr, paral_tree_num_curr = _get_tree_nums(bst_curr)
-
-        bst_prev["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
-            "num_trees"
-        ] = str(tree_num_prev + paral_tree_num_curr)
-        iteration_indptr = bst_prev["learner"]["gradient_booster"]["model"][
-            "iteration_indptr"
-        ]
-        bst_prev["learner"]["gradient_booster"]["model"]["iteration_indptr"].append(
-            iteration_indptr[-1] + paral_tree_num_curr
+    if not bst_prev_org:
+        return bst_curr_org
+
+    # Get the tree numbers
+    tree_num_prev, _ = _get_tree_nums(bst_prev_org)
+    _, paral_tree_num_curr = _get_tree_nums(bst_curr_org)
+
+    bst_prev = json.loads(bytearray(bst_prev_org))
+    bst_curr = json.loads(bytearray(bst_curr_org))
+
+    bst_prev["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+        "num_trees"
+    ] = str(tree_num_prev + paral_tree_num_curr)
+    iteration_indptr = bst_prev["learner"]["gradient_booster"]["model"][
+        "iteration_indptr"
+    ]
+    bst_prev["learner"]["gradient_booster"]["model"]["iteration_indptr"].append(
+        iteration_indptr[-1] + paral_tree_num_curr
+    )
+
+    # Aggregate new trees
+    trees_curr = bst_curr["learner"]["gradient_booster"]["model"]["trees"]
+    for tree_count in range(paral_tree_num_curr):
+        trees_curr[tree_count]["id"] = tree_num_prev + tree_count
+        bst_prev["learner"]["gradient_booster"]["model"]["trees"].append(
+            trees_curr[tree_count]
         )
+        bst_prev["learner"]["gradient_booster"]["model"]["tree_info"].append(0)
+
+    bst_prev_bytes = bytes(json.dumps(bst_prev), "utf-8")
 
-        # Aggregate new trees
-        trees_curr = bst_curr["learner"]["gradient_booster"]["model"]["trees"]
-        for tree_count in range(paral_tree_num_curr):
-            trees_curr[tree_count]["id"] = tree_num_prev + tree_count
-            bst_prev["learner"]["gradient_booster"]["model"]["trees"].append(
-                trees_curr[tree_count]
-            )
-            bst_prev["learner"]["gradient_booster"]["model"]["tree_info"].append(0)
-        return bst_prev
+    return bst_prev_bytes
 
 
-def _get_tree_nums(xgb_model: Dict) -> (int, int):
+def _get_tree_nums(xgb_model_org: bytes) -> Tuple[int, int]:
+    xgb_model = json.loads(bytearray(xgb_model_org))
     # Get the number of trees
     tree_num = int(
         xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"][

From 1400d12aa7591feb546713896ee09ba9598aed07 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 17 Nov 2023 09:40:24 +0000
Subject: [PATCH 22/64] Add baselines to README (#2613)

---
 README.md | 32 ++++++++++++++++++++------------
 1 file changed, 20 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
index 4f148d2445d9..b8b62e8c0c43 100644
--- a/README.md
+++ b/README.md
@@ -91,18 +91,26 @@ Stay tuned, more tutorials are coming soon. Topics include **Privacy and Securit
 
 ## Flower Baselines
 
-Flower Baselines is a collection of community-contributed experiments that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas:
-
-- [FedAvg](https://arxiv.org/abs/1602.05629):
-  - [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist)
-- [FedProx](https://arxiv.org/abs/1812.06127):
-  - [MNIST](https://github.com/adap/flower/tree/main/baselines/fedprox/)
-- [Adaptive Federated Optimization](https://arxiv.org/abs/2003.00295):
-  - [CIFAR-10/100](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization)
-
-Check the Flower documentation to learn more: [Using Baselines](https://flower.dev/docs/baselines/how-to-use-baselines.html)
-
-The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline: [Contributing Baselines](https://flower.dev/docs/baselines/how-to-contribute-baselines.html)
+Flower Baselines is a collection of community-contributed projects that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas. The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline!
+
+- [DASHA](https://github.com/adap/flower/tree/main/baselines/dasha)
+- [DepthFL](https://github.com/adap/flower/tree/main/baselines/depthfl)
+- [FedBN](https://github.com/adap/flower/tree/main/baselines/fedbn)
+- [FedMeta](https://github.com/adap/flower/tree/main/baselines/fedmeta)
+- [FedMLB](https://github.com/adap/flower/tree/main/baselines/fedmlb)
+- [FedPer](https://github.com/adap/flower/tree/main/baselines/fedper)
+- [FedProx](https://github.com/adap/flower/tree/main/baselines/fedprox)
+- [FedWav2vec2](https://github.com/adap/flower/tree/main/baselines/fedwav2vec2)
+- [FjORD](https://github.com/adap/flower/tree/main/baselines/fjord)
+- [MOON](https://github.com/adap/flower/tree/main/baselines/moon)
+- [niid-Bench](https://github.com/adap/flower/tree/main/baselines/niid_bench)
+- [TAMUNA](https://github.com/adap/flower/tree/main/baselines/tamuna)
+- [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist)
+- [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization)
+
+Please refer to the [Flower Baselines Documentation](https://flower.dev/docs/baselines/) for a detailed categorization of baselines and for additional info including:
+* [How to use Flower Baselines](https://flower.dev/docs/baselines/how-to-use-baselines.html)
+* [How to contribute a new Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html)
 
 ## Flower Usage Examples
 

From 8b4d7a7e0c996f3346c31923f1591ddee8240b67 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 17 Nov 2023 09:45:20 +0000
Subject: [PATCH 23/64] Update content of template_baseline (#2616)

---
 baselines/baseline_template/README.md         | 28 +++++++++----------
 .../baseline_template/main.py                 |  4 +++
 2 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/baselines/baseline_template/README.md b/baselines/baseline_template/README.md
index 682952717426..ee6e1e96976f 100644
--- a/baselines/baseline_template/README.md
+++ b/baselines/baseline_template/README.md
@@ -1,11 +1,11 @@
 ---
 title: title of the paper
 url: URL to the paper page (not the pdf)
-labels: [label1, label2] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. "system heterogeneity", "image classification", "asynchronous", "weight sharing", "cross-silo")
-dataset: [dataset1, dataset2] # list of datasets you include in your baseline
+labels: [label1, label2] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. system heterogeneity, image classification, asynchronous, weight sharing, cross-silo). Do not use ""
+dataset: [dataset1, dataset2] # list of datasets you include in your baseline. Do not use ""
 ---
 
-# :warning:*_Title of your baseline_*
+# :warning: *_Title of your baseline_*
 
 > Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
 
@@ -15,33 +15,33 @@ dataset: [dataset1, dataset2] # list of datasets you include in your baseline
 
 > :warning: Please complete the metadata section at the very top of this README. This generates a table at the top of the file that will facilitate indexing baselines.
 
-****Paper:**** :warning: *_add the URL of the paper page (not to the .pdf). For instance if you link a paper on ArXiv, add here the URL to the abstract page (e.g. https://arxiv.org/abs/1512.03385). If your paper is in from a journal or conference proceedings, please follow the same logic._*
+**Paper:** :warning: *_add the URL of the paper page (not to the .pdf). For instance if you link a paper on ArXiv, add here the URL to the abstract page (e.g. https://arxiv.org/abs/1512.03385). If your paper is in from a journal or conference proceedings, please follow the same logic._*
 
-****Authors:**** :warning: *_list authors of the paper_*
+**Authors:** :warning: *_list authors of the paper_*
 
-****Abstract:**** :warning: *_add here the abstract of the paper you are implementing_*
+**Abstract:** :warning: *_add here the abstract of the paper you are implementing_*
 
 
 ## About this baseline
 
-****What’s implemented:**** :warning: *_Concisely describe what experiment(s) in the publication can be replicated by running the code. Please only use a few sentences. Start with: “The code in this directory …”_*
+**What’s implemented:** :warning: *_Concisely describe what experiment(s) in the publication can be replicated by running the code. Please only use a few sentences. Start with: “The code in this directory …”_*
 
-****Datasets:**** :warning: *_List the datasets you used (if you used a medium to large dataset, >10GB please also include the sizes of the dataset)._*
+**Datasets:** :warning: *_List the datasets you used (if you used a medium to large dataset, >10GB please also include the sizes of the dataset)._*
 
-****Hardware Setup:**** :warning: *_Give some details about the hardware (e.g. a server with 8x V100 32GB and 256GB of RAM) you used to run the experiments for this baseline. Someone out there might not have access to the same resources you have so, could list the absolute minimum hardware needed to run the experiment in a reasonable amount of time ? (e.g. minimum is 1x 16GB GPU otherwise a client model can’t be trained with a sufficiently large batch size). Could you test this works too?_*
+**Hardware Setup:** :warning: *_Give some details about the hardware (e.g. a server with 8x V100 32GB and 256GB of RAM) you used to run the experiments for this baseline. Someone out there might not have access to the same resources you have so, could list the absolute minimum hardware needed to run the experiment in a reasonable amount of time ? (e.g. minimum is 1x 16GB GPU otherwise a client model can’t be trained with a sufficiently large batch size). Could you test this works too?_*
 
-****Contributors:**** :warning: *_let the world know who contributed to this baseline. This could be either your name, your name and affiliation at the time, or your GitHub profile name if you prefer. If multiple contributors signed up for this baseline, please list yourself and your colleagues_*
+**Contributors:** :warning: *_let the world know who contributed to this baseline. This could be either your name, your name and affiliation at the time, or your GitHub profile name if you prefer. If multiple contributors signed up for this baseline, please list yourself and your colleagues_*
 
 
 ## Experimental Setup
 
-****Task:**** :warning: *_what’s the primary task that is being federated? (e.g. image classification, next-word prediction). If you have experiments for several, please list them_*
+**Task:** :warning: *_what’s the primary task that is being federated? (e.g. image classification, next-word prediction). If you have experiments for several, please list them_*
 
-****Model:**** :warning: *_provide details about the model you used in your experiments (if more than use a list). If your model is small, describing it as a table would be :100:. Some FL methods do not use an off-the-shelve model (e.g. ResNet18) instead they create your own. If this is your case, please provide a summary here and give pointers to where in the paper (e.g. Appendix B.4) is detailed._*
+**Model:** :warning: *_provide details about the model you used in your experiments (if more than use a list). If your model is small, describing it as a table would be :100:. Some FL methods do not use an off-the-shelve model (e.g. ResNet18) instead they create your own. If this is your case, please provide a summary here and give pointers to where in the paper (e.g. Appendix B.4) is detailed._*
 
-****Dataset:**** :warning: *_Earlier you listed already the datasets that your baseline uses. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if  your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table._*
+**Dataset:** :warning: *_Earlier you listed already the datasets that your baseline uses. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if  your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table._*
 
-****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._*
+**Training Hyperparameters:** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._*
 
 
 ## Environment Setup
diff --git a/baselines/baseline_template/baseline_template/main.py b/baselines/baseline_template/baseline_template/main.py
index 795800c64e21..25ae1bec6a10 100644
--- a/baselines/baseline_template/baseline_template/main.py
+++ b/baselines/baseline_template/baseline_template/main.py
@@ -51,3 +51,7 @@ def main(cfg: DictConfig) -> None:
     # Hydra will generate for you a directory each time you run the code. You
     # can retrieve the path to that directory with this:
     # save_path = HydraConfig.get().runtime.output_dir
+
+
+if __name__ == "__main__":
+    main()

From 77b715a614ac9a2d740272cc513a86f8b0fb1b7a Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 17 Nov 2023 10:03:17 +0000
Subject: [PATCH 24/64] Update baseline_request.yml (#2614)

---
 .github/ISSUE_TEMPLATE/baseline_request.yml | 32 +++++++--------------
 1 file changed, 11 insertions(+), 21 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/baseline_request.yml b/.github/ISSUE_TEMPLATE/baseline_request.yml
index ec4b1e8a9898..49ae922a94ad 100644
--- a/.github/ISSUE_TEMPLATE/baseline_request.yml
+++ b/.github/ISSUE_TEMPLATE/baseline_request.yml
@@ -41,32 +41,22 @@ body:
         - [ ]  Read the [`first contribution` doc](https://flower.dev/docs/first-time-contributors.html)
         - [ ]  Complete the Flower tutorial
         - [ ]  Read the Flower Baselines docs to get an overview:
-            - [ ]  [https://flower.dev/docs/using-baselines.html](https://flower.dev/docs/using-baselines.html)
-            - [ ]  [https://flower.dev/docs/contributing-baselines.html](https://flower.dev/docs/contributing-baselines.html)
+            - [ ]  [How to use Flower Baselines](https://flower.dev/docs/baselines/how-to-use-baselines.html)
+            - [ ]  [How to contribute a Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html)
   - type: checkboxes
     attributes:
       label: Prepare - understand the scope
       options:
         - label: Read the paper linked above
-        - label: Create the directory structure in Flower Baselines (just the `__init__.py` files and a `README.md`)
-        - label: Before starting to write code, write down all of the specs of this experiment in a README (dataset, partitioning, model, number of clients, all hyperparameters, …)
-        - label: Open a draft PR
+        - label: Decide which experiments you'd like to reproduce. The more the better!
+        - label: Follow the steps outlined in [Add a new Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html#add-a-new-flower-baseline).
+        - label: You can use as reference [other baselines](https://github.com/adap/flower/tree/main/baselines) that the community merged following those steps.
   - type: checkboxes
     attributes:
-      label: Implement - make it work
+      label: Verify your implementation
       options:
-        - label: Implement some form of dataset loading and partitioning in a separate `dataset.py` (doesn’t have to match the paper exactly)
-        - label: Implement the model in PyTorch
-        - label: Write a test that shows that the model has the number of parameters mentioned in the paper
-        - label: Implement the federated learning setup outlined in the paper, maybe starting with fewer clients
-        - label: Plot accuracy and loss
-        - label: Run it and check if the model starts to converge
-  - type: checkboxes
-    attributes:
-      label: Align - make it converge
-      options:
-        - label: Implement the exact data partitioning outlined in the paper
-        - label: Use the exact hyperparameters outlined in the paper
-        - label: Make it converge to roughly the same accuracy that the paper states
-        - label: Commit the final hyperparameters and plots
-        - label: Mark the PR as ready
+        - label: Follow the steps indicated in the `EXTENDED_README.md` that was created in your baseline directory
+        - label: Ensure your code reproduces the results for the experiments you chose
+        - label: Ensure your `README.md` is ready to be run by someone that is no familiar with your code. Are all step-by-step instructions clear?
+        - label: Ensure running the formatting and typing tests for your baseline runs without errors.
+        - label: Clone your repo on a new directory, follow the guide on your own `README.md` and verify everything runs.

From b43515cc90f05fd8d708ba78fc8ab402b7f2ed4d Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 17 Nov 2023 10:08:21 +0000
Subject: [PATCH 25/64] Do minor fixes to FedBN (#2615)

---
 baselines/fedbn/README.md                     |  2 +-
 baselines/fedbn/fedbn/client.py               | 36 ++++++++++++-------
 baselines/fedbn/fedbn/conf/client/fedavg.yaml |  3 +-
 baselines/fedbn/fedbn/conf/client/fedbn.yaml  |  3 +-
 baselines/fedbn/fedbn/dataset.py              | 17 ++++++---
 baselines/fedbn/fedbn/main.py                 | 20 +++--------
 baselines/fedbn/fedbn/models.py               |  4 +--
 doc/source/ref-changelog.md                   |  2 +-
 8 files changed, 48 insertions(+), 39 deletions(-)

diff --git a/baselines/fedbn/README.md b/baselines/fedbn/README.md
index cc4f68b90e9e..4b271bd49851 100644
--- a/baselines/fedbn/README.md
+++ b/baselines/fedbn/README.md
@@ -59,7 +59,7 @@ A more detailed explanation of the datasets is given in the following table.
 | strategy_fraction_fit | 1.0 |
 | strategy.fraction_evaluate | 0.0 |
 | training samples per client| 743 |
-| lr | 10E-2 |
+| client.l_r | 10E-2 |
 | local epochs | 1 |
 | loss | cross entropy loss |
 | optimizer | SGD |
diff --git a/baselines/fedbn/fedbn/client.py b/baselines/fedbn/fedbn/client.py
index be3b4b8f54a7..ce498b8cebc5 100644
--- a/baselines/fedbn/fedbn/client.py
+++ b/baselines/fedbn/fedbn/client.py
@@ -16,17 +16,18 @@
 
 
 class FlowerClient(fl.client.NumPyClient):
-    """A standar FlowerClient. This base class.
+    """A standard FlowerClient.
 
-    is what plain FedAvg clients do.
+    This base class is what plain FedAvg clients do.
     """
 
-    def __init__(
+    def __init__(  # pylint: disable=too-many-arguments
         self,
         model: CNNModel,
         trainloader: DataLoader,
         testloader: DataLoader,
         dataset_name: str,
+        l_r: float,
         **kwargs,  # pylint: disable=unused-argument
     ) -> None:
         self.trainloader = trainloader
@@ -34,6 +35,7 @@ def __init__(
         self.dataset_name = dataset_name
         self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         self.model = model.to(self.device)
+        self.l_r = l_r
 
     def get_parameters(self, config) -> NDArrays:
         """Return model parameters as a list of NumPy ndarrays w or w/o.
@@ -58,22 +60,23 @@ def fit(
         """Set model parameters, train model, return updated model parameters."""
         self.set_parameters(parameters)
 
-        # evaluate the state of the global model on the train set; the loss returned
+        # Evaluate the state of the global model on the train set; the loss returned
         # is what's reported in Fig3 in the FedBN paper (what this baseline focuses
         # in reproducing)
         pre_train_loss, pre_train_acc = test(
             self.model, self.trainloader, device=self.device
         )
 
-        # train model on local dataset
+        # Train model on local dataset
         loss, acc = train(
             self.model,
             self.trainloader,
             epochs=1,
+            l_r=self.l_r,
             device=self.device,
         )
 
-        # construct metrics to return to server
+        # Construct metrics to return to server
         fl_round = config["round"]
         metrics = {
             "dataset_name": self.dataset_name,
@@ -107,9 +110,16 @@ def evaluate(
 class FedBNFlowerClient(FlowerClient):
     """Similar to FlowerClient but this is used by FedBN clients."""
 
-    def __init__(self, bn_state_dir: Path, client_id: int, *args, **kwargs) -> None:
+    def __init__(self, save_path: Path, client_id: int, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
-        self.bn_state_dir = bn_state_dir
+        # For FedBN clients we need to persist the state of the BN
+        # layers across rounds. In Simulation clients are statess
+        # so everything not communicated to the server (as it is the
+        # case as with params in BN layers of FedBN clients) is lost
+        # once a client completes its training. An upcoming version of
+        # Flower suports stateful clients
+        bn_state_dir = save_path / "bn_states"
+        bn_state_dir.mkdir(exist_ok=True)
         self.bn_state_pkl = bn_state_dir / f"client_{client_id}.pkl"
 
     def _save_bn_statedict(self) -> None:
@@ -135,7 +145,7 @@ def get_parameters(self, config) -> NDArrays:
 
         layers.
         """
-        # first update bn_state_dir
+        # First update bn_state_dir
         self._save_bn_statedict()
         # Excluding parameters of BN layers when using FedBN
         return [
@@ -154,8 +164,8 @@ def set_parameters(self, parameters: NDArrays) -> None:
         state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
         self.model.load_state_dict(state_dict, strict=False)
 
-        # now also load from bn_state_dir
-        if self.bn_state_pkl.exists():  # it won't exist in the first round
+        # Now also load from bn_state_dir
+        if self.bn_state_pkl.exists():  # It won't exist in the first round
             bn_state_dict = self._load_bn_statedict()
             self.model.load_state_dict(bn_state_dict, strict=False)
 
@@ -164,7 +174,7 @@ def gen_client_fn(
     client_data: List[Tuple[DataLoader, DataLoader, str]],
     client_cfg: DictConfig,
     model_cfg: DictConfig,
-    bn_state_dir: Path,
+    save_path: Path,
 ) -> Callable[[str], FlowerClient]:
     """Return a function that will be called to instantiate the cid-th client."""
 
@@ -182,7 +192,7 @@ def client_fn(cid: str) -> FlowerClient:
             trainloader=trainloader,
             testloader=valloader,
             dataset_name=dataset_name,
-            bn_state_dir=bn_state_dir,
+            save_path=save_path,
             client_id=int(cid),
         )
 
diff --git a/baselines/fedbn/fedbn/conf/client/fedavg.yaml b/baselines/fedbn/fedbn/conf/client/fedavg.yaml
index a08fccc6d24e..993fcb7b4d38 100644
--- a/baselines/fedbn/fedbn/conf/client/fedavg.yaml
+++ b/baselines/fedbn/fedbn/conf/client/fedavg.yaml
@@ -1,4 +1,5 @@
 ---
 # standard FedAvg Flower Client
 _target_: fedbn.client.FlowerClient
-client_label: FedAvg
\ No newline at end of file
+client_label: FedAvg
+l_r: 0.01
\ No newline at end of file
diff --git a/baselines/fedbn/fedbn/conf/client/fedbn.yaml b/baselines/fedbn/fedbn/conf/client/fedbn.yaml
index 0f0e2a05cbfb..26b6833062a5 100644
--- a/baselines/fedbn/fedbn/conf/client/fedbn.yaml
+++ b/baselines/fedbn/fedbn/conf/client/fedbn.yaml
@@ -1,4 +1,5 @@
 ---
 # standard FedBN Flower Client
 _target_: fedbn.client.FedBNFlowerClient
-client_label: FedBN
\ No newline at end of file
+client_label: FedBN
+l_r: 0.01
\ No newline at end of file
diff --git a/baselines/fedbn/fedbn/dataset.py b/baselines/fedbn/fedbn/dataset.py
index 49fdad166430..8ce3eb520f89 100644
--- a/baselines/fedbn/fedbn/dataset.py
+++ b/baselines/fedbn/fedbn/dataset.py
@@ -24,7 +24,7 @@ def __init__(  # pylint: disable=too-many-arguments
         transform=None,
     ):
         if train and partitions is not None:
-            # construct dataset by loading one or more partitions
+            # Construct dataset by loading one or more partitions
             self.images, self.labels = np.load(
                 os.path.join(
                     data_path,
@@ -212,11 +212,18 @@ def get_data(dataset_cfg: DictConfig) -> List[Tuple[DataLoader, DataLoader, str]
     client_data = []
     d_cfg = dataset_cfg
 
-    allowed_percent = (np.arange(1, 11) / 10).tolist()
+    total_partitions = (
+        10  # each dataset was pre-processed by the authors and split into 10 partitions
+    )
+    # First check that percent used is allowed
+    allowed_percent = (np.arange(1, total_partitions + 1) / total_partitions).tolist()
     assert d_cfg.percent in allowed_percent, (
         f"'dataset.percent' should be in {allowed_percent}."
         "\nThis is because the trainset is pre-partitioned into 10 disjoint sets."
     )
+
+    # Then check that with the percent selected, the desired number of clients (and
+    # therefore dataloaders) can be created.
     max_expected_clients = len(d_cfg.to_include) * 1 / d_cfg.percent
 
     num_clients_step = len(d_cfg.to_include)
@@ -234,10 +241,10 @@ def get_data(dataset_cfg: DictConfig) -> List[Tuple[DataLoader, DataLoader, str]
         "'dataset.percent' you chose."
     )
 
+    # All good, then create as many dataloaders as clients in the experiment.
+    # Each dataloader might containe one or more partitions (depends on 'percent')
+    # Each dataloader contains data of the same dataset.
     num_clients_per_dataset = d_cfg.num_clients // num_clients_step
-    total_partitions = (
-        10  # each dataset was pre-processed by the authors and split into 10 partitions
-    )
     num_parts = int(d_cfg.percent * total_partitions)
 
     for dataset_name in dataset_cfg.to_include:
diff --git a/baselines/fedbn/fedbn/main.py b/baselines/fedbn/fedbn/main.py
index 8942a175052d..15fd4a436bc8 100644
--- a/baselines/fedbn/fedbn/main.py
+++ b/baselines/fedbn/fedbn/main.py
@@ -3,8 +3,7 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
-# these are the basic packages you'll need here
-# feel free to remove some if aren't needed
+
 import pickle
 from pathlib import Path
 
@@ -33,16 +32,7 @@ def main(cfg: DictConfig) -> None:
 
     # Hydra automatically creates an output directory
     # Let's retrieve it and save some results there
-    save_path = HydraConfig.get().runtime.output_dir
-
-    # For FedBN clients we need to persist the state of the BN
-    # layers across rounds. In Simulation clients are statess
-    # so everything not communicated to the server (as it is the
-    # case as with params in BN layers of FedBN clients) is lost
-    # once a client completes its training. An upcoming version of
-    # Flower suports stateful clients
-    bn_states = Path(save_path) / "bn_states"
-    bn_states.mkdir()
+    save_path = Path(HydraConfig.get().runtime.output_dir)
 
     # 2. Prepare your dataset
     # please ensure you followed the README.md and you downloaded the
@@ -50,7 +40,7 @@ def main(cfg: DictConfig) -> None:
     client_data_loaders = get_data(cfg.dataset)
 
     # 3. Define your client generation function
-    client_fn = gen_client_fn(client_data_loaders, cfg.client, cfg.model, bn_states)
+    client_fn = gen_client_fn(client_data_loaders, cfg.client, cfg.model, save_path)
 
     # 4. Define your strategy
     strategy = instantiate(cfg.strategy)
@@ -71,14 +61,14 @@ def main(cfg: DictConfig) -> None:
     print("................")
     print(history)
 
-    # save results as a Python pickle using a file_path
+    # Save results as a Python pickle using a file_path
     # the directory created by Hydra for each run
     data = {"history": history}
     history_path = f"{str(save_path)}/history.pkl"
     with open(history_path, "wb") as handle:
         pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)
 
-    # simple plot
+    # Simple plot
     quick_plot(history_path)
 
 
diff --git a/baselines/fedbn/fedbn/models.py b/baselines/fedbn/fedbn/models.py
index efe982905123..c15a8e1815e6 100644
--- a/baselines/fedbn/fedbn/models.py
+++ b/baselines/fedbn/fedbn/models.py
@@ -56,11 +56,11 @@ def forward(self, x):
 
 
 # pylint: disable=too-many-locals
-def train(model, traindata, epochs, device) -> Tuple[float, float]:
+def train(model, traindata, epochs, l_r, device) -> Tuple[float, float]:
     """Train the network."""
     # Define loss and optimizer
     criterion = nn.CrossEntropyLoss()
-    optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
+    optimizer = torch.optim.SGD(model.parameters(), lr=l_r)
 
     # Train the network
     model.to(device)
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 10112088b88c..dd8a6e225401 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -46,7 +46,7 @@
 
   - niid-Bench [#2428](https://github.com/adap/flower/pull/2428)
 
-  - FedBN [#2608](https://github.com/adap/flower/pull/2608)
+  - FedBN ([#2608](https://github.com/adap/flower/pull/2608), [#2615](https://github.com/adap/flower/pull/2615))
 
 - **Update Flower Examples** ([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425), [#2526](https://github.com/adap/flower/pull/2526))
 

From bc346ac910b01f30d77397155248a6ada22bad46 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Fri, 17 Nov 2023 11:08:04 +0000
Subject: [PATCH 26/64] Make `flower-server` start server with HTTPS by default
 (#2591)

Co-authored-by: Daniel J. Beutel <daniel@flower.dev>
Co-authored-by: Taner Topal <taner@flower.dev>
---
 .github/workflows/e2e.yml       |  4 +-
 e2e/bare-https/README.md        |  3 ++
 e2e/bare-https/certificate.conf | 20 ++++++++++
 e2e/bare-https/client.py        | 35 +++++++++++++++++
 e2e/bare-https/driver.py        | 12 ++++++
 e2e/bare-https/generate.sh      | 55 +++++++++++++++++++++++++++
 e2e/bare-https/pyproject.toml   | 13 +++++++
 e2e/bare-https/server.py        | 15 ++++++++
 e2e/bare-https/simulation.py    |  1 +
 e2e/test.sh                     |  4 ++
 e2e/test_driver.sh              | 12 +++++-
 src/py/flwr/server/app.py       | 66 +++++++++++++++++++++++++++++++--
 12 files changed, 234 insertions(+), 6 deletions(-)
 create mode 100644 e2e/bare-https/README.md
 create mode 100644 e2e/bare-https/certificate.conf
 create mode 100644 e2e/bare-https/client.py
 create mode 100644 e2e/bare-https/driver.py
 create mode 100755 e2e/bare-https/generate.sh
 create mode 100644 e2e/bare-https/pyproject.toml
 create mode 100644 e2e/bare-https/server.py
 create mode 100644 e2e/bare-https/simulation.py

diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 6189794e8f69..a5121ad71b38 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -60,6 +60,8 @@ jobs:
         include:
           - directory: bare
 
+          - directory: bare-https
+
           - directory: jax
 
           - directory: pytorch
@@ -135,7 +137,7 @@ jobs:
       - name: Run virtual client test
         run: python simulation.py
       - name: Run driver test
-        run: ./../test_driver.sh
+        run: ./../test_driver.sh "${{ matrix.directory }}"
 
   strategies:
     runs-on: ubuntu-22.04
diff --git a/e2e/bare-https/README.md b/e2e/bare-https/README.md
new file mode 100644
index 000000000000..2b7fb953a24b
--- /dev/null
+++ b/e2e/bare-https/README.md
@@ -0,0 +1,3 @@
+# Bare Flower testing
+
+This directory is used for testing Flower in a bare minimum scenario, that is, with a dummy model and dummy operations. This is mainly to test the core functionnality of Flower independently from any framework. It can easily be extendended to test more complex communication set-ups.
diff --git a/e2e/bare-https/certificate.conf b/e2e/bare-https/certificate.conf
new file mode 100644
index 000000000000..ea97fcbb700d
--- /dev/null
+++ b/e2e/bare-https/certificate.conf
@@ -0,0 +1,20 @@
+[req]
+default_bits = 4096
+prompt = no
+default_md = sha256
+req_extensions = req_ext
+distinguished_name = dn
+
+[dn]
+C = DE
+ST = HH
+O = Flower
+CN = localhost
+
+[req_ext]
+subjectAltName = @alt_names
+
+[alt_names]
+DNS.1 = localhost
+IP.1 = ::1
+IP.2 = 127.0.0.1
diff --git a/e2e/bare-https/client.py b/e2e/bare-https/client.py
new file mode 100644
index 000000000000..da04a320b1d2
--- /dev/null
+++ b/e2e/bare-https/client.py
@@ -0,0 +1,35 @@
+import flwr as fl
+import numpy as np
+from pathlib import Path
+
+
+model_params = np.array([1])
+objective = 5
+
+# Define Flower client
+class FlowerClient(fl.client.NumPyClient):
+    def get_parameters(self, config):
+        return model_params
+
+    def fit(self, parameters, config):
+        model_params = parameters
+        model_params = [param * (objective/np.mean(param)) for param in model_params]
+        return model_params, 1, {}
+
+    def evaluate(self, parameters, config):
+        model_params = parameters
+        loss = min(np.abs(1 - np.mean(model_params)/objective), 1)
+        accuracy = 1 - loss
+        return loss, 1, {"accuracy": accuracy}
+
+def client_fn(cid):
+    return FlowerClient()
+
+
+if __name__ == "__main__":
+    # Start Flower client
+    fl.client.start_numpy_client(
+        server_address="127.0.0.1:8080", 
+        client=FlowerClient(),
+        root_certificates=Path("certificates/ca.crt").read_bytes(),
+    )
diff --git a/e2e/bare-https/driver.py b/e2e/bare-https/driver.py
new file mode 100644
index 000000000000..5c44e4c641ae
--- /dev/null
+++ b/e2e/bare-https/driver.py
@@ -0,0 +1,12 @@
+import flwr as fl
+from pathlib import Path
+
+
+# Start Flower server
+hist = fl.driver.start_driver(
+    server_address="127.0.0.1:9091",
+    config=fl.server.ServerConfig(num_rounds=3),
+    root_certificates=Path("certificates/ca.crt").read_bytes(),
+)
+
+assert hist.losses_distributed[-1][1] == 0
diff --git a/e2e/bare-https/generate.sh b/e2e/bare-https/generate.sh
new file mode 100755
index 000000000000..72362656e756
--- /dev/null
+++ b/e2e/bare-https/generate.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+# This script will generate all certificates if ca.crt does not exist
+
+set -e
+# Change directory to the script's directory
+cd "$(dirname "${BASH_SOURCE[0]}")"
+
+CA_PASSWORD=notsafe
+
+CERT_DIR=certificates
+
+# Generate directories if not exists
+mkdir -p $CERT_DIR
+
+# Uncomment the below block if you want to skip certificate generation if they already exist.
+# if [ -f "$CERT_DIR/ca.crt" ]; then
+#     echo "Skipping certificate generation as they already exist."
+#     exit 0
+# fi
+
+# Clearing any existing files in the certificates directory
+rm -f $CERT_DIR/*
+
+# Generate the root certificate authority key and certificate based on key
+openssl genrsa -out $CERT_DIR/ca.key 4096
+openssl req \
+    -new \
+    -x509 \
+    -key $CERT_DIR/ca.key \
+    -sha256 \
+    -subj "/C=DE/ST=HH/O=CA, Inc." \
+    -days 365 -out $CERT_DIR/ca.crt
+
+# Generate a new private key for the server
+openssl genrsa -out $CERT_DIR/server.key 4096
+
+# Create a signing CSR
+openssl req \
+    -new \
+    -key $CERT_DIR/server.key \
+    -out $CERT_DIR/server.csr \
+    -config certificate.conf
+
+# Generate a certificate for the server
+openssl x509 \
+    -req \
+    -in $CERT_DIR/server.csr \
+    -CA $CERT_DIR/ca.crt \
+    -CAkey $CERT_DIR/ca.key \
+    -CAcreateserial \
+    -out $CERT_DIR/server.pem \
+    -days 365 \
+    -sha256 \
+    -extfile certificate.conf \
+    -extensions req_ext
diff --git a/e2e/bare-https/pyproject.toml b/e2e/bare-https/pyproject.toml
new file mode 100644
index 000000000000..9489a43195f9
--- /dev/null
+++ b/e2e/bare-https/pyproject.toml
@@ -0,0 +1,13 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "bare_https_test"
+version = "0.1.0"
+description = "HTTPS-enabled bare Federated Learning test with Flower"
+authors = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+flwr = { path = "../../", develop = true }
diff --git a/e2e/bare-https/server.py b/e2e/bare-https/server.py
new file mode 100644
index 000000000000..fcad7a3e4522
--- /dev/null
+++ b/e2e/bare-https/server.py
@@ -0,0 +1,15 @@
+import flwr as fl
+from pathlib import Path
+
+
+hist = fl.server.start_server(
+    server_address="127.0.0.1:8080",
+    config=fl.server.ServerConfig(num_rounds=3),
+    certificates=(
+        Path("certificates/ca.crt").read_bytes(),
+        Path("certificates/server.pem").read_bytes(),
+        Path("certificates/server.key").read_bytes(),
+    )
+)
+
+assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98
diff --git a/e2e/bare-https/simulation.py b/e2e/bare-https/simulation.py
new file mode 100644
index 000000000000..b7268c98dcbc
--- /dev/null
+++ b/e2e/bare-https/simulation.py
@@ -0,0 +1 @@
+# No simulation test for bare-https
diff --git a/e2e/test.sh b/e2e/test.sh
index c1f8d3177113..4ea17a4f994b 100755
--- a/e2e/test.sh
+++ b/e2e/test.sh
@@ -5,6 +5,10 @@ case "$1" in
   pandas)
     server_file="server.py"
     ;;
+  bare-https)
+    ./generate.sh
+    server_file="server.py"
+    ;;
   *)
     server_file="../server.py"
     ;;
diff --git a/e2e/test_driver.sh b/e2e/test_driver.sh
index 3ca95e90d321..e5baac20fa1f 100755
--- a/e2e/test_driver.sh
+++ b/e2e/test_driver.sh
@@ -1,7 +1,17 @@
 #!/bin/bash
 set -e
 
-timeout 2m flower-server --grpc-bidi --grpc-bidi-fleet-api-address 0.0.0.0:8080 &
+case "$1" in
+  bare-https)
+    ./generate.sh
+    cert_arg="--certificates certificates/ca.crt certificates/server.pem certificates/server.key"
+    ;;
+  *)
+    cert_arg="--insecure"
+    ;;
+esac
+
+timeout 2m flower-server $cert_arg --grpc-bidi --grpc-bidi-fleet-api-address 0.0.0.0:8080 &
 sleep 3
 
 python client.py &
diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index 8d1317aba403..63c24c37a685 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -22,6 +22,7 @@
 from dataclasses import dataclass
 from logging import ERROR, INFO, WARN
 from os.path import isfile
+from pathlib import Path
 from signal import SIGINT, SIGTERM, signal
 from types import FrameType
 from typing import List, Optional, Tuple
@@ -247,6 +248,9 @@ def run_driver_api() -> None:
     host, port, is_v6 = parsed_address
     address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}"
 
+    # Obtain certificates
+    certificates = _try_obtain_certificates(args)
+
     # Initialize StateFactory
     state_factory = StateFactory(args.database)
 
@@ -254,6 +258,7 @@ def run_driver_api() -> None:
     grpc_server: grpc.Server = _run_driver_api_grpc(
         address=address,
         state_factory=state_factory,
+        certificates=certificates,
     )
 
     # Graceful shutdown
@@ -273,6 +278,9 @@ def run_fleet_api() -> None:
     event(EventType.RUN_FLEET_API_ENTER)
     args = _parse_args_fleet().parse_args()
 
+    # Obtain certificates
+    certificates = _try_obtain_certificates(args)
+
     # Initialize StateFactory
     state_factory = StateFactory(args.database)
 
@@ -315,6 +323,7 @@ def run_fleet_api() -> None:
         fleet_server = _run_fleet_api_grpc_bidi(
             address=address,
             state_factory=state_factory,
+            certificates=certificates,
         )
         grpc_servers.append(fleet_server)
     elif args.fleet_api_type == TRANSPORT_TYPE_GRPC_RERE:
@@ -327,6 +336,7 @@ def run_fleet_api() -> None:
         fleet_server = _run_fleet_api_grpc_rere(
             address=address,
             state_factory=state_factory,
+            certificates=certificates,
         )
         grpc_servers.append(fleet_server)
     else:
@@ -346,7 +356,7 @@ def run_fleet_api() -> None:
         bckg_threads[0].join()
 
 
-# pylint: disable=too-many-branches
+# pylint: disable=too-many-branches, too-many-locals, too-many-statements
 def run_server() -> None:
     """Run Flower server (Driver API and Fleet API)."""
     log(INFO, "Starting Flower server")
@@ -360,6 +370,9 @@ def run_server() -> None:
     host, port, is_v6 = parsed_address
     address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}"
 
+    # Obtain certificates
+    certificates = _try_obtain_certificates(args)
+
     # Initialize StateFactory
     state_factory = StateFactory(args.database)
 
@@ -367,6 +380,7 @@ def run_server() -> None:
     driver_server: grpc.Server = _run_driver_api_grpc(
         address=address,
         state_factory=state_factory,
+        certificates=certificates,
     )
 
     grpc_servers = [driver_server]
@@ -408,6 +422,7 @@ def run_server() -> None:
         fleet_server = _run_fleet_api_grpc_bidi(
             address=address,
             state_factory=state_factory,
+            certificates=certificates,
         )
         grpc_servers.append(fleet_server)
     elif args.fleet_api_type == TRANSPORT_TYPE_GRPC_RERE:
@@ -420,6 +435,7 @@ def run_server() -> None:
         fleet_server = _run_fleet_api_grpc_rere(
             address=address,
             state_factory=state_factory,
+            certificates=certificates,
         )
         grpc_servers.append(fleet_server)
     else:
@@ -441,6 +457,29 @@ def run_server() -> None:
         driver_server.wait_for_termination(timeout=1)
 
 
+def _try_obtain_certificates(
+    args: argparse.Namespace,
+) -> Optional[Tuple[bytes, bytes, bytes]]:
+    # Obtain certificates
+    if args.insecure:
+        log(WARN, "Option `--insecure` was set. Starting insecure HTTP server.")
+        certificates = None
+    # Check if certificates are provided
+    elif args.certificates:
+        certificates = (
+            Path(args.certificates[0]).read_bytes(),  # CA certificate
+            Path(args.certificates[1]).read_bytes(),  # server certificate
+            Path(args.certificates[2]).read_bytes(),  # server private key
+        )
+    else:
+        sys.exit(
+            "Certificates are required unless running in insecure mode. "
+            "Please provide certificate paths with '--certificates' or run the server "
+            "in insecure mode using '--insecure' if you understand the risks."
+        )
+    return certificates
+
+
 def _register_exit_handlers(
     grpc_servers: List[grpc.Server],
     bckg_threads: List[threading.Thread],
@@ -490,6 +529,7 @@ def graceful_exit_handler(  # type: ignore
 def _run_driver_api_grpc(
     address: str,
     state_factory: StateFactory,
+    certificates: Optional[Tuple[bytes, bytes, bytes]],
 ) -> grpc.Server:
     """Run Driver API (gRPC, request-response)."""
     # Create Driver API gRPC server
@@ -501,7 +541,7 @@ def _run_driver_api_grpc(
         servicer_and_add_fn=(driver_servicer, driver_add_servicer_to_server_fn),
         server_address=address,
         max_message_length=GRPC_MAX_MESSAGE_LENGTH,
-        certificates=None,
+        certificates=certificates,
     )
 
     log(INFO, "Flower ECE: Starting Driver API (gRPC-rere) on %s", address)
@@ -513,6 +553,7 @@ def _run_driver_api_grpc(
 def _run_fleet_api_grpc_bidi(
     address: str,
     state_factory: StateFactory,
+    certificates: Optional[Tuple[bytes, bytes, bytes]],
 ) -> grpc.Server:
     """Run Fleet API (gRPC, bidirectional streaming)."""
     # DriverClientManager
@@ -529,7 +570,7 @@ def _run_fleet_api_grpc_bidi(
         servicer_and_add_fn=(fleet_servicer, fleet_add_servicer_to_server_fn),
         server_address=address,
         max_message_length=GRPC_MAX_MESSAGE_LENGTH,
-        certificates=None,
+        certificates=certificates,
     )
 
     log(INFO, "Flower ECE: Starting Fleet API (gRPC-bidi) on %s", address)
@@ -541,6 +582,7 @@ def _run_fleet_api_grpc_bidi(
 def _run_fleet_api_grpc_rere(
     address: str,
     state_factory: StateFactory,
+    certificates: Optional[Tuple[bytes, bytes, bytes]],
 ) -> grpc.Server:
     """Run Fleet API (gRPC, request-response)."""
     # Create Fleet API gRPC server
@@ -552,7 +594,7 @@ def _run_fleet_api_grpc_rere(
         servicer_and_add_fn=(fleet_servicer, fleet_add_servicer_to_server_fn),
         server_address=address,
         max_message_length=GRPC_MAX_MESSAGE_LENGTH,
-        certificates=None,
+        certificates=certificates,
     )
 
     log(INFO, "Flower ECE: Starting Fleet API (gRPC-rere) on %s", address)
@@ -684,6 +726,22 @@ def _parse_args_server() -> argparse.ArgumentParser:
 
 
 def _add_args_common(parser: argparse.ArgumentParser) -> None:
+    parser.add_argument(
+        "--insecure",
+        action="store_true",
+        help="Run the server without HTTPS, regardless of whether certificate "
+        "paths are provided. By default, the server runs with HTTPS enabled. "
+        "Use this flag only if you understand the risks.",
+    )
+    parser.add_argument(
+        "--certificates",
+        nargs=3,
+        metavar=("CA_CERT", "SERVER_CERT", "PRIVATE_KEY"),
+        type=str,
+        help="Paths to the CA certificate, server certificate, and server private "
+        "key, in that order. Note: The server can only be started without "
+        "certificates by enabling the `--insecure` flag.",
+    )
     parser.add_argument(
         "--database",
         help="A string representing the path to the database "

From 5cf5c827abd5ba70cab0c9dce306733780920da6 Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Fri, 17 Nov 2023 12:48:21 +0000
Subject: [PATCH 27/64] Create XGBoost-quickstart example (#2612)

Co-authored-by: yan-gao-GY <yan@flower.dev>
---
 examples/xgboost-quickstart/README.md        |  86 +++++++++
 examples/xgboost-quickstart/client.py        | 173 +++++++++++++++++++
 examples/xgboost-quickstart/pyproject.toml   |  15 ++
 examples/xgboost-quickstart/requirements.txt |   3 +
 examples/xgboost-quickstart/run.sh           |  17 ++
 examples/xgboost-quickstart/server.py        |  37 ++++
 6 files changed, 331 insertions(+)
 create mode 100644 examples/xgboost-quickstart/README.md
 create mode 100644 examples/xgboost-quickstart/client.py
 create mode 100644 examples/xgboost-quickstart/pyproject.toml
 create mode 100644 examples/xgboost-quickstart/requirements.txt
 create mode 100755 examples/xgboost-quickstart/run.sh
 create mode 100644 examples/xgboost-quickstart/server.py

diff --git a/examples/xgboost-quickstart/README.md b/examples/xgboost-quickstart/README.md
new file mode 100644
index 000000000000..53cd37e18aa3
--- /dev/null
+++ b/examples/xgboost-quickstart/README.md
@@ -0,0 +1,86 @@
+# Flower Example using XGBoost
+
+This example demonstrates how to perform EXtreme Gradient Boosting (XGBoost) within Flower using `xgboost` package.
+Tree-based with bagging method is used for aggregation on the server.
+
+This project provides a minimal code example to enable you to get stated quickly. For a more comprehensive code example, take a look at [xgboost-comprehensive](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive).
+
+## Project Setup
+
+Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you:
+
+```shell
+git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/xgboost-quickstart . && rm -rf flower && cd xgboost-quickstart
+```
+
+This will create a new directory called `xgboost-quickstart` containing the following files:
+
+```
+-- README.md         <- Your're reading this right now
+-- server.py         <- Defines the server-side logic
+-- client.py         <- Defines the client-side logic
+-- pyproject.toml    <- Example dependencies (if you use Poetry)
+-- requirements.txt  <- Example dependencies
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `xgboost` and `flwr`) are defined in `pyproject.toml` and `requirements.txt`. We recommend [Poetry](https://python-poetry.org/docs/) to install those dependencies and manage your virtual environment ([Poetry installation](https://python-poetry.org/docs/#installation)) or [pip](https://pip.pypa.io/en/latest/development/), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.
+
+#### Poetry
+
+```shell
+poetry install
+poetry shell
+```
+
+Poetry will install all your dependencies in a newly created virtual environment. To verify that everything works correctly you can run the following command:
+
+```shell
+poetry run python3 -c "import flwr"
+```
+
+If you don't see any errors you're good to go!
+
+#### pip
+
+Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt.
+
+```shell
+pip install -r requirements.txt
+```
+
+## Run Federated Learning with XGBoost and Flower
+
+Afterwards you are ready to start the Flower server as well as the clients.
+You can simply start the server in a terminal as follows:
+
+```shell
+python3 server.py
+```
+
+Now you are ready to start the Flower clients which will participate in the learning.
+To do so simply open two more terminal windows and run the following commands.
+
+Start client 1 in the first terminal:
+
+```shell
+python3 client.py --node-id=0
+```
+
+Start client 2 in the second terminal:
+
+```shell
+python3 client.py --node-id=1
+```
+
+You will see that XGBoost is starting a federated training.
+
+Alternatively, you can use `run.sh` to run the same experiment in a single terminal as follows:
+
+```shell
+bash run.sh
+```
+
+Look at the [code](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)
+and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
diff --git a/examples/xgboost-quickstart/client.py b/examples/xgboost-quickstart/client.py
new file mode 100644
index 000000000000..ede4a2bba764
--- /dev/null
+++ b/examples/xgboost-quickstart/client.py
@@ -0,0 +1,173 @@
+import argparse
+import warnings
+from typing import Union
+from logging import INFO
+from datasets import Dataset, DatasetDict
+import xgboost as xgb
+
+import flwr as fl
+from flwr_datasets import FederatedDataset
+from flwr.common.logger import log
+from flwr.common import (
+    Code,
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    GetParametersIns,
+    GetParametersRes,
+    Parameters,
+    Status,
+)
+from flwr_datasets.partitioner import IidPartitioner
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+# Define arguments parser for the client/node ID.
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--node-id",
+    default=0,
+    type=int,
+    help="Node ID used for the current client.",
+)
+args = parser.parse_args()
+
+
+# Define data partitioning related functions
+def train_test_split(partition: Dataset, test_fraction: float, seed: int):
+    """Split the data into train and validation set given split rate."""
+    train_test = partition.train_test_split(test_size=test_fraction, seed=seed)
+    partition_train = train_test["train"]
+    partition_test = train_test["test"]
+
+    num_train = len(partition_train)
+    num_test = len(partition_test)
+
+    return partition_train, partition_test, num_train, num_test
+
+
+def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.DMatrix:
+    """Transform dataset to DMatrix format for xgboost."""
+    x = data["inputs"]
+    y = data["label"]
+    new_data = xgb.DMatrix(x, label=y)
+    return new_data
+
+
+# Load (HIGGS) dataset and conduct partitioning
+partitioner = IidPartitioner(num_partitions=2)
+fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})
+
+# Load the partition for this `node_id`
+partition = fds.load_partition(idx=args.node_id, split="train")
+partition.set_format("numpy")
+
+# Train/test splitting
+train_data, valid_data, num_train, num_val = train_test_split(
+    partition, test_fraction=0.2, seed=42
+)
+
+# Reformat data to DMatrix for xgboost
+train_dmatrix = transform_dataset_to_dmatrix(train_data)
+valid_dmatrix = transform_dataset_to_dmatrix(valid_data)
+
+# Hyper-parameters for xgboost training
+num_local_round = 1
+params = {
+    "objective": "binary:logistic",
+    "eta": 0.1,  # Learning rate
+    "max_depth": 8,
+    "eval_metric": "auc",
+    "nthread": 16,
+    "num_parallel_tree": 1,
+    "subsample": 1,
+    "tree_method": "hist",
+}
+
+
+# Define Flower client
+class FlowerClient(fl.client.Client):
+    def __init__(self):
+        self.bst = None
+        self.config = None
+
+    def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
+        _ = (self, ins)
+        return GetParametersRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[]),
+        )
+
+    def _local_boost(self):
+        # Update trees based on local training data.
+        for i in range(num_local_round):
+            self.bst.update(train_dmatrix, self.bst.num_boosted_rounds())
+
+        # Extract the last N=num_local_round trees for sever aggregation
+        bst = self.bst[
+            self.bst.num_boosted_rounds()
+            - num_local_round : self.bst.num_boosted_rounds()
+        ]
+
+        return bst
+
+    def fit(self, ins: FitIns) -> FitRes:
+        if not self.bst:
+            # First round local training
+            log(INFO, "Start training at round 1")
+            bst = xgb.train(
+                params,
+                train_dmatrix,
+                num_boost_round=num_local_round,
+                evals=[(valid_dmatrix, "validate"), (train_dmatrix, "train")],
+            )
+            self.config = bst.save_config()
+            self.bst = bst
+        else:
+            for item in ins.parameters.tensors:
+                global_model = bytearray(item)
+
+            # Load global model into booster
+            self.bst.load_model(global_model)
+            self.bst.load_config(self.config)
+
+            bst = self._local_boost()
+
+        local_model = bst.save_raw("json")
+        local_model_bytes = bytes(local_model)
+
+        return FitRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[local_model_bytes]),
+            num_examples=num_train,
+            metrics={},
+        )
+
+    def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
+        eval_results = self.bst.eval_set(
+            evals=[(valid_dmatrix, "valid")],
+            iteration=self.bst.num_boosted_rounds() - 1,
+        )
+        auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4)
+
+        return EvaluateRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            loss=0.0,
+            num_examples=num_val,
+            metrics={"AUC": auc},
+        )
+
+
+# Start Flower client
+fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient())
diff --git a/examples/xgboost-quickstart/pyproject.toml b/examples/xgboost-quickstart/pyproject.toml
new file mode 100644
index 000000000000..74256846c693
--- /dev/null
+++ b/examples/xgboost-quickstart/pyproject.toml
@@ -0,0 +1,15 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "xgboost-quickstart"
+version = "0.1.0"
+description = "Federated XGBoost with Flower (quickstart)"
+authors = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+flwr-datasets = ">=0.0.1,<1.0.0"
+xgboost = ">=2.0.0,<3.0.0"
diff --git a/examples/xgboost-quickstart/requirements.txt b/examples/xgboost-quickstart/requirements.txt
new file mode 100644
index 000000000000..9596a8d6cd02
--- /dev/null
+++ b/examples/xgboost-quickstart/requirements.txt
@@ -0,0 +1,3 @@
+flwr>=1.0, <2.0
+flwr-datasets>=0.0.1, <1.0.0
+xgboost>=2.0.0, <3.0.0
diff --git a/examples/xgboost-quickstart/run.sh b/examples/xgboost-quickstart/run.sh
new file mode 100755
index 000000000000..6287145bfb5f
--- /dev/null
+++ b/examples/xgboost-quickstart/run.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
+
+echo "Starting server"
+python server.py &
+sleep 5  # Sleep for 5s to give the server enough time to start
+
+for i in `seq 0 1`; do
+    echo "Starting client $i"
+    python3 client.py --node-id=$i &
+done
+
+# Enable CTRL+C to stop all background processes
+trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+# Wait for all background processes to complete
+wait
diff --git a/examples/xgboost-quickstart/server.py b/examples/xgboost-quickstart/server.py
new file mode 100644
index 000000000000..b45a375ce94f
--- /dev/null
+++ b/examples/xgboost-quickstart/server.py
@@ -0,0 +1,37 @@
+import flwr as fl
+from flwr.server.strategy import FedXgbBagging
+
+
+# FL experimental settings
+pool_size = 2
+num_rounds = 5
+num_clients_per_round = 2
+num_evaluate_clients = 2
+
+
+def evaluate_metrics_aggregation(eval_metrics):
+    """Return an aggregated metric (AUC) for evaluation."""
+    total_num = sum([num for num, _ in eval_metrics])
+    auc_aggregated = (
+        sum([metrics["AUC"] * num for num, metrics in eval_metrics]) / total_num
+    )
+    metrics_aggregated = {"AUC": auc_aggregated}
+    return metrics_aggregated
+
+
+# Define strategy
+strategy = FedXgbBagging(
+    fraction_fit=(float(num_clients_per_round) / pool_size),
+    min_fit_clients=num_clients_per_round,
+    min_available_clients=pool_size,
+    min_evaluate_clients=num_evaluate_clients,
+    fraction_evaluate=1.0,
+    evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation,
+)
+
+# Start Flower server
+fl.server.start_server(
+    server_address="0.0.0.0:8080",
+    config=fl.server.ServerConfig(num_rounds=num_rounds),
+    strategy=strategy,
+)

From e8c94659e5b855999aaa1ca715b181aed291621b Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Sun, 19 Nov 2023 19:31:10 +0000
Subject: [PATCH 28/64] Update xgboost-comprehensive example and some small
 fixes (#2617)

Co-authored-by: yan-gao-GY <yan@flower.dev>
---
 examples/xgboost-comprehensive/README.md      |  62 ++++++++++--------
 .../_static/xgboost_flower_auc.png            | Bin 0 -> 68480 bytes
 examples/xgboost-comprehensive/client.py      |  19 ++----
 examples/xgboost-comprehensive/run.sh         |   6 +-
 examples/xgboost-comprehensive/server.py      |  13 +---
 examples/xgboost-comprehensive/utils.py       |  12 ++++
 6 files changed, 56 insertions(+), 56 deletions(-)
 create mode 100644 examples/xgboost-comprehensive/_static/xgboost_flower_auc.png

diff --git a/examples/xgboost-comprehensive/README.md b/examples/xgboost-comprehensive/README.md
index 3b31e23cb321..783438188dab 100644
--- a/examples/xgboost-comprehensive/README.md
+++ b/examples/xgboost-comprehensive/README.md
@@ -1,7 +1,13 @@
-# Flower Example using XGBoost
+# Flower Example using XGBoost (Comprehensive)
 
-This example demonstrates how to perform EXtreme Gradient Boosting (XGBoost) within Flower using `xgboost` package.
-Tree-based with bagging method is used for aggregation on the server.
+This example demonstrates a comprehensive federated learning setup using Flower with XGBoost.
+It differs from the quickstart example in the following ways:
+
+- Arguments parsers of server and clients for hyperparameters selection.
+- Customised FL settings.
+- Customised number of partitions.
+- Customised partitioner type (uniform, linear, square, exponential).
+- Centralised/distributed evaluation.
 
 ## Project Setup
 
@@ -18,7 +24,8 @@ This will create a new directory called `xgboost-comprehensive` containing the f
 -- server.py         <- Defines the server-side logic
 -- client.py         <- Defines the client-side logic
 -- dataset.py        <- Defines the functions of data loading and partitioning
--- utils.py          <- Defines the arguments parser for clients and server.
+-- utils.py          <- Defines the arguments parser for clients and server
+-- run.sh            <- Commands to run experiments
 -- pyproject.toml    <- Example dependencies (if you use Poetry)
 -- requirements.txt  <- Example dependencies
 ```
@@ -52,36 +59,35 @@ pip install -r requirements.txt
 
 ## Run Federated Learning with XGBoost and Flower
 
-Afterwards you are ready to start the Flower server as well as the clients.
-You can simply start the server in a terminal as follows:
+The included `run.sh` will start the Flower server (using `server.py`) with centralised evaluation,
+sleep for 15 seconds to ensure that the server is up,
+and then start 5 Flower clients (using `client.py`) with a small subset of the data from exponential partition distribution.
+You can simply start everything in a terminal as follows:
 
 ```shell
-python3 server.py
+poetry run ./run.sh
 ```
 
-Now you are ready to start the Flower clients which will participate in the learning.
-To do so simply open two more terminal windows and run the following commands.
-
-Start client 1 in the first terminal:
+The `run.sh` script starts processes in the background so that you don't have to open eleven terminal windows.
+If you experiment with the code example and something goes wrong, simply using `CTRL + C` on Linux (or `CMD + C` on macOS) wouldn't normally kill all these processes,
+which is why the script ends with `trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM EXIT` and `wait`.
+This simply allows you to stop the experiment using `CTRL + C` (or `CMD + C`).
+If you change the script and anything goes wrong you can still use `killall python` (or `killall python3`)
+to kill all background processes (or a more specific command if you have other Python processes running that you don't want to kill).
 
-```shell
-python3 client.py --node-id=0
-```
-
-Start client 2 in the second terminal:
-
-```shell
-python3 client.py --node-id=1
-```
+You can also manually run `poetry run python3 server.py --pool-size=N --num-clients-per-round=N`
+and `poetry run python3 client.py --node-id=NODE_ID --num-partitions=N` for as many clients as you want,
+but you have to make sure that each command is run in a different terminal window (or a different computer on the network).
 
-You will see that XGBoost is starting a federated training.
+In addition, we provide more options to customise the experimental settings, including data partitioning and centralised/distributed evaluation (see `utils.py`).
+Look at the [code](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive)
+and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
 
-Alternatively, you can use `run.sh` to run the same experiment in a single terminal as follows:
+### Expected Experimental Results
 
-```shell
-bash run.sh
-```
+![](_static/xgboost_flower_auc.png)
 
-Besides, we provide options to customise the experimental settings, including data partitioning and centralised/distributed evaluation (see `utils.py`).
-Look at the [code](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive)
-and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
+The figure above shows the centralised tested AUC performance over FL rounds on 4 experimental settings.
+One can see that all settings obtain stable performance boost over FL rounds (especially noticeable at the start of training).
+As expected, uniform client distribution shows higher AUC values (beyond 83% at the end) than square/exponential setup.
+Feel free to explore more interesting experiments by yourself!
diff --git a/examples/xgboost-comprehensive/_static/xgboost_flower_auc.png b/examples/xgboost-comprehensive/_static/xgboost_flower_auc.png
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HcmV?d00001

diff --git a/examples/xgboost-comprehensive/client.py b/examples/xgboost-comprehensive/client.py
index 5aba30266b5a..889f5f47e0ae 100644
--- a/examples/xgboost-comprehensive/client.py
+++ b/examples/xgboost-comprehensive/client.py
@@ -23,7 +23,7 @@
     transform_dataset_to_dmatrix,
     resplit,
 )
-from utils import client_args_parser
+from utils import client_args_parser, BST_PARAMS
 
 
 warnings.filterwarnings("ignore", category=UserWarning)
@@ -46,7 +46,7 @@
     dataset="jxie/higgs", partitioners={"train": partitioner}, resplitter=resplit
 )
 
-# Let's use the first partition as an example
+# Load the partition for this `node_id`
 node_id = args.node_id
 partition = fds.load_partition(idx=node_id, split="train")
 partition.set_format("numpy")
@@ -73,20 +73,11 @@
 
 # Hyper-parameters for xgboost training
 num_local_round = 1
-params = {
-    "objective": "binary:logistic",
-    "eta": 0.1,  # Learning rate
-    "max_depth": 8,
-    "eval_metric": "auc",
-    "nthread": 16,
-    "num_parallel_tree": 1,
-    "subsample": 1,
-    "tree_method": "hist",
-}
+params = BST_PARAMS
 
 
 # Define Flower client
-class FlowerClient(fl.client.Client):
+class XgbClient(fl.client.Client):
     def __init__(self):
         self.bst = None
         self.config = None
@@ -171,4 +162,4 @@ def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
 
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient())
+fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient())
diff --git a/examples/xgboost-comprehensive/run.sh b/examples/xgboost-comprehensive/run.sh
index 7cf65fa4d52d..7920f6bf5e55 100755
--- a/examples/xgboost-comprehensive/run.sh
+++ b/examples/xgboost-comprehensive/run.sh
@@ -3,12 +3,12 @@ set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
 
 echo "Starting server"
-python server.py &
+python3 server.py --pool-size=5 --num-rounds=50 --num-clients-per-round=5 --centralised-eval &
 sleep 15  # Sleep for 15s to give the server enough time to start
 
-for i in `seq 0 1`; do
+for i in `seq 0 4`; do
     echo "Starting client $i"
-    python3 client.py --node-id=$i &
+    python3 client.py --node-id=$i --num-partitions=5 --partitioner-type=exponential &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/xgboost-comprehensive/server.py b/examples/xgboost-comprehensive/server.py
index e4c597ee17eb..3da7e8d9865c 100644
--- a/examples/xgboost-comprehensive/server.py
+++ b/examples/xgboost-comprehensive/server.py
@@ -8,7 +8,7 @@
 from flwr_datasets import FederatedDataset
 from flwr.server.strategy import FedXgbBagging
 
-from utils import server_args_parser
+from utils import server_args_parser, BST_PARAMS
 from dataset import resplit, transform_dataset_to_dmatrix
 
 
@@ -30,16 +30,7 @@
     test_dmatrix = transform_dataset_to_dmatrix(test_set)
 
 # Hyper-parameters used for initialisation
-params = {
-    "objective": "binary:logistic",
-    "eta": 0.1,  # Learning rate
-    "max_depth": 8,
-    "eval_metric": "auc",
-    "nthread": 16,
-    "num_parallel_tree": 1,
-    "subsample": 1,
-    "tree_method": "hist",
-}
+params = BST_PARAMS
 
 
 def eval_config(rnd: int) -> Dict[str, str]:
diff --git a/examples/xgboost-comprehensive/utils.py b/examples/xgboost-comprehensive/utils.py
index 51c1a1b9604d..000def370752 100644
--- a/examples/xgboost-comprehensive/utils.py
+++ b/examples/xgboost-comprehensive/utils.py
@@ -1,6 +1,18 @@
 import argparse
 
 
+BST_PARAMS = {
+    "objective": "binary:logistic",
+    "eta": 0.1,  # Learning rate
+    "max_depth": 8,
+    "eval_metric": "auc",
+    "nthread": 16,
+    "num_parallel_tree": 1,
+    "subsample": 1,
+    "tree_method": "hist",
+}
+
+
 def client_args_parser():
     """Parse arguments to define experimental settings on client side."""
     parser = argparse.ArgumentParser()

From 6d8c0feaab16e0e92a077f9cd3a789fa55cf99e5 Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Sun, 19 Nov 2023 19:35:48 +0000
Subject: [PATCH 29/64] Update xgboost-quickstart readme (#2618)

Co-authored-by: yan-gao-GY <yan@flower.dev>
---
 examples/xgboost-quickstart/README.md | 3 ++-
 examples/xgboost-quickstart/client.py | 4 ++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/examples/xgboost-quickstart/README.md b/examples/xgboost-quickstart/README.md
index 53cd37e18aa3..309f8f55b847 100644
--- a/examples/xgboost-quickstart/README.md
+++ b/examples/xgboost-quickstart/README.md
@@ -19,6 +19,7 @@ This will create a new directory called `xgboost-quickstart` containing the foll
 -- README.md         <- Your're reading this right now
 -- server.py         <- Defines the server-side logic
 -- client.py         <- Defines the client-side logic
+-- run.sh            <- Commands to run experiments
 -- pyproject.toml    <- Example dependencies (if you use Poetry)
 -- requirements.txt  <- Example dependencies
 ```
@@ -79,7 +80,7 @@ You will see that XGBoost is starting a federated training.
 Alternatively, you can use `run.sh` to run the same experiment in a single terminal as follows:
 
 ```shell
-bash run.sh
+poetry run ./run.sh
 ```
 
 Look at the [code](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)
diff --git a/examples/xgboost-quickstart/client.py b/examples/xgboost-quickstart/client.py
index ede4a2bba764..ed85e98a3ab2 100644
--- a/examples/xgboost-quickstart/client.py
+++ b/examples/xgboost-quickstart/client.py
@@ -88,7 +88,7 @@ def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.
 
 
 # Define Flower client
-class FlowerClient(fl.client.Client):
+class XgbClient(fl.client.Client):
     def __init__(self):
         self.bst = None
         self.config = None
@@ -170,4 +170,4 @@ def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
 
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient())
+fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient())

From 459c7fcff5ef671cb917e6e8da32acdd6ef55d82 Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Mon, 20 Nov 2023 09:59:12 +0000
Subject: [PATCH 30/64] XGBoost tutorial (#2567)

Co-authored-by: yan-gao-GY <yan@flower.dev>
---
 doc/source/tutorial-quickstart-xgboost.rst | 770 ++++++++++++++++++++-
 1 file changed, 768 insertions(+), 2 deletions(-)

diff --git a/doc/source/tutorial-quickstart-xgboost.rst b/doc/source/tutorial-quickstart-xgboost.rst
index be7094614c63..3de4e0ef81cb 100644
--- a/doc/source/tutorial-quickstart-xgboost.rst
+++ b/doc/source/tutorial-quickstart-xgboost.rst
@@ -7,6 +7,772 @@ Quickstart XGBoost
 .. meta::
    :description: Check out this Federated Learning quickstart tutorial for using Flower with XGBoost to train classification models on trees.
 
-Let's build a horizontal federated learning system using XGBoost and Flower!
+Federated XGBoost
+-------------
 
-Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart-xgboost-horizontal>`_ to learn more.
+EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient implementation of gradient-boosted decision tree (**GBDT**), that maximises the computational boundaries for boosted tree methods.
+It's primarily designed to enhance both the performance and computational speed of machine learning models.
+In XGBoost, trees are constructed concurrently, unlike the sequential approach taken by GBDT.
+
+Often, for tabular data on medium-sized datasets with fewer than 10k training examples, XGBoost surpasses the results of deep learning techniques.
+
+Why federated XGBoost?
+~~~~~~~~
+
+Indeed, as the demand for data privacy and decentralized learning grows, there's an increasing requirement to implement federated XGBoost systems for specialised applications, like survival analysis and financial fraud detection.
+
+Federated learning ensures that raw data remains on the local device, making it an attractive approach for sensitive domains where data security and privacy are paramount.
+Given the robustness and efficiency of XGBoost, combining it with federated learning offers a promising solution for these specific challenges.
+
+In this tutorial we will learn how to train a federated XGBoost model on HIGGS dataset using Flower and :code:`xgboost` package.
+We use a simple example (`full code xgboost-quickstart <https://github.com/adap/flower/tree/main/examples/xgboost-quickstart>`_) with two *clients* and one *server*
+to demonstrate how federated XGBoost works,
+and then we dive into a more complex example (`full code xgboost-comprehensive <https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive>`_) to run various experiments.
+
+
+Environment Setup
+-------------
+
+First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_.
+
+We first need to install Flower and Flower Datasets. You can do this by running :
+
+.. code-block:: shell
+
+  $ pip install flwr flwr-datasets
+
+Since we want to use :code:`xgboost` package to build up XGBoost trees, let's go ahead and install :code:`xgboost`:
+
+.. code-block:: shell
+
+  $ pip install xgboost
+
+
+Flower Client
+-------------
+
+*Clients* are responsible for generating individual weight-updates for the model based on their local datasets.
+Now that we have all our dependencies installed, let's run a simple distributed training with two clients and one server.
+
+In a file called :code:`client.py`, import xgboost, Flower, Flower Datasets and other related functions:
+
+.. code-block:: python
+
+    import argparse
+    from typing import Union
+    from logging import INFO
+    from datasets import Dataset, DatasetDict
+    import xgboost as xgb
+
+    import flwr as fl
+    from flwr_datasets import FederatedDataset
+    from flwr.common.logger import log
+    from flwr.common import (
+        Code,
+        EvaluateIns,
+        EvaluateRes,
+        FitIns,
+        FitRes,
+        GetParametersIns,
+        GetParametersRes,
+        Parameters,
+        Status,
+    )
+    from flwr_datasets.partitioner import IidPartitioner
+
+Dataset partition and hyper-parameter selection
+~~~~~~~~
+
+Prior to local training, we require loading the HIGGS dataset from Flower Datasets and conduct data partitioning for FL:
+
+.. code-block:: python
+
+    # Load (HIGGS) dataset and conduct partitioning
+    partitioner = IidPartitioner(num_partitions=2)
+    fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})
+
+    # Load the partition for this `node_id`
+    partition = fds.load_partition(idx=args.node_id, split="train")
+    partition.set_format("numpy")
+
+In this example, we split the dataset into two partitions with uniform distribution (:code:`IidPartitioner(num_partitions=2)`).
+Then, we load the partition for the given client based on :code:`node_id`:
+
+.. code-block:: python
+
+    # We first define arguments parser for user to specify the client/node ID.
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--node-id",
+        default=0,
+        type=int,
+        help="Node ID used for the current client.",
+    )
+    args = parser.parse_args()
+
+    # Load the partition for this `node_id`.
+    partition = fds.load_partition(idx=args.node_id, split="train")
+    partition.set_format("numpy")
+
+After that, we do train/test splitting on the given partition (client's local data), and transform data format for :code:`xgboost` package.
+
+.. code-block:: python
+
+    # Train/test splitting
+    train_data, valid_data, num_train, num_val = train_test_split(
+        partition, test_fraction=0.2, seed=42
+    )
+
+    # Reformat data to DMatrix for xgboost
+    train_dmatrix = transform_dataset_to_dmatrix(train_data)
+    valid_dmatrix = transform_dataset_to_dmatrix(valid_data)
+
+The functions of :code:`train_test_split` and :code:`transform_dataset_to_dmatrix` are defined as below:
+
+.. code-block:: python
+
+    # Define data partitioning related functions
+    def train_test_split(partition: Dataset, test_fraction: float, seed: int):
+        """Split the data into train and validation set given split rate."""
+        train_test = partition.train_test_split(test_size=test_fraction, seed=seed)
+        partition_train = train_test["train"]
+        partition_test = train_test["test"]
+
+        num_train = len(partition_train)
+        num_test = len(partition_test)
+
+        return partition_train, partition_test, num_train, num_test
+
+
+    def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.DMatrix:
+        """Transform dataset to DMatrix format for xgboost."""
+        x = data["inputs"]
+        y = data["label"]
+        new_data = xgb.DMatrix(x, label=y)
+        return new_data
+
+Finally, we define the hyper-parameters used for XGBoost training.
+
+.. code-block:: python
+
+    num_local_round = 1
+    params = {
+        "objective": "binary:logistic",
+        "eta": 0.1,  # lr
+        "max_depth": 8,
+        "eval_metric": "auc",
+        "nthread": 16,
+        "num_parallel_tree": 1,
+        "subsample": 1,
+        "tree_method": "hist",
+    }
+
+The :code:`num_local_round` represents the number of iterations for local tree boost.
+We use CPU for the training in default.
+One can shift it to GPU by setting :code:`tree_method` to :code:`gpu_hist`.
+We use AUC as evaluation metric.
+
+
+Flower client definition for XGBoost
+~~~~~~~~
+
+After loading the dataset we define the Flower client.
+We follow the general rule to define :code:`XgbClient` class inherited from :code:`fl.client.Client`.
+
+.. code-block:: python
+
+    class XgbClient(fl.client.Client):
+        def __init__(self):
+            self.bst = None
+            self.config = None
+
+The :code:`self.bst` is used to keep the Booster objects that remain consistent across rounds,
+allowing them to store predictions from trees integrated in earlier rounds and maintain other essential data structures for training.
+
+Then, we override :code:`get_parameters`, :code:`fit` and :code:`evaluate` methods insides :code:`XgbClient` class as follows.
+
+.. code-block:: python
+
+    def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
+        _ = (self, ins)
+        return GetParametersRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[]),
+        )
+
+Unlike neural network training, XGBoost trees are not started from a specified random weights.
+In this case, we do not use :code:`get_parameters` and :code:`set_parameters` to initialise model parameters for XGBoost.
+As a result, let's return an empty tensor in :code:`get_parameters` when it is called by the server at the first round.
+
+.. code-block:: python
+
+    def fit(self, ins: FitIns) -> FitRes:
+        if not self.bst:
+            # First round local training
+            log(INFO, "Start training at round 1")
+            bst = xgb.train(
+                params,
+                train_dmatrix,
+                num_boost_round=num_local_round,
+                evals=[(valid_dmatrix, "validate"), (train_dmatrix, "train")],
+            )
+            self.config = bst.save_config()
+            self.bst = bst
+        else:
+            for item in ins.parameters.tensors:
+                global_model = bytearray(item)
+
+            # Load global model into booster
+            self.bst.load_model(global_model)
+            self.bst.load_config(self.config)
+
+            bst = self._local_boost()
+
+        local_model = bst.save_raw("json")
+        local_model_bytes = bytes(local_model)
+
+        return FitRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[local_model_bytes]),
+            num_examples=num_train,
+            metrics={},
+        )
+
+In :code:`fit`, at the first round, we call :code:`xgb.train()` to build up the first set of trees.
+the returned Booster object and config are stored in :code:`self.bst` and :code:`self.config`, respectively.
+From the second round, we load the global model sent from server to :code:`self.bst`,
+and then update model weights on local training data with function :code:`local_boost` as follows:
+
+.. code-block:: python
+
+    def _local_boost(self):
+        # Update trees based on local training data.
+        for i in range(num_local_round):
+            self.bst.update(train_dmatrix, self.bst.num_boosted_rounds())
+
+        # Extract the last N=num_local_round trees for sever aggregation
+        bst = self.bst[
+            self.bst.num_boosted_rounds()
+            - num_local_round : self.bst.num_boosted_rounds()
+        ]
+
+Given :code:`num_local_round`, we update trees by calling :code:`self.bst.update` method.
+After training, the last :code:`N=num_local_round` trees will be extracted to send to the server.
+
+.. code-block:: python
+
+    def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
+        eval_results = self.bst.eval_set(
+            evals=[(valid_dmatrix, "valid")],
+            iteration=self.bst.num_boosted_rounds() - 1,
+        )
+        auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4)
+
+        return EvaluateRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            loss=0.0,
+            num_examples=num_val,
+            metrics={"AUC": auc},
+        )
+
+In :code:`evaluate`, we call :code:`self.bst.eval_set` function to conduct evaluation on valid set.
+The AUC value will be returned.
+
+Now, we can create an instance of our class :code:`XgbClient` and add one line to actually run this client:
+
+.. code-block:: python
+
+    fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient())
+
+That's it for the client. We only have to implement :code:`Client`and call :code:`fl.client.start_client()`.
+The string :code:`"[::]:8080"` tells the client which server to connect to.
+In our case we can run the server and the client on the same machine, therefore we use
+:code:`"[::]:8080"`. If we run a truly federated workload with the server and
+clients running on different machines, all that needs to change is the
+:code:`server_address` we point the client at.
+
+
+Flower Server
+-------------
+
+These updates are then sent to the *server* which will aggregate them to produce a better model.
+Finally, the *server* sends this improved version of the model back to each *client* to finish a complete FL round.
+
+In a file named :code:`server.py`, import Flower and FedXgbBagging from :code:`flwr.server.strategy`.
+
+We first define a strategy for XGBoost bagging aggregation.
+
+.. code-block:: python
+
+    # Define strategy
+    strategy = FedXgbBagging(
+        fraction_fit=1.0,
+        min_fit_clients=2,
+        min_available_clients=2,
+        min_evaluate_clients=2,
+        fraction_evaluate=1.0,
+        evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation,
+    )
+
+    def evaluate_metrics_aggregation(eval_metrics):
+        """Return an aggregated metric (AUC) for evaluation."""
+        total_num = sum([num for num, _ in eval_metrics])
+        auc_aggregated = (
+            sum([metrics["AUC"] * num for num, metrics in eval_metrics]) / total_num
+        )
+        metrics_aggregated = {"AUC": auc_aggregated}
+        return metrics_aggregated
+
+We use two clients for this example.
+An :code:`evaluate_metrics_aggregation` function is defined to collect and wighted average the AUC values from clients.
+
+Then, we start the server:
+
+.. code-block:: python
+
+    # Start Flower server
+    fl.server.start_server(
+        server_address="0.0.0.0:8080",
+        config=fl.server.ServerConfig(num_rounds=num_rounds),
+        strategy=strategy,
+    )
+
+Tree-based bagging aggregation
+~~~~~~~~
+
+You must be curious about how bagging aggregation works. Let's look into the details.
+
+In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define :code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`.
+Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` and :code:`evaluate` methods as follows:
+
+.. code-block:: python
+
+    import json
+    from logging import WARNING
+    from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
+
+    from flwr.common import EvaluateRes, FitRes, Parameters, Scalar
+    from flwr.common.logger import log
+    from flwr.server.client_proxy import ClientProxy
+
+    from .fedavg import FedAvg
+
+
+    class FedXgbBagging(FedAvg):
+        """Configurable FedXgbBagging strategy implementation."""
+
+        def __init__(
+            self,
+            evaluate_function: Optional[
+                Callable[
+                    [int, Parameters, Dict[str, Scalar]],
+                    Optional[Tuple[float, Dict[str, Scalar]]],
+                ]
+            ] = None,
+            **kwargs: Any,
+        ):
+            self.evaluate_function = evaluate_function
+            self.global_model: Optional[bytes] = None
+            super().__init__(**kwargs)
+
+        def aggregate_fit(
+            self,
+            server_round: int,
+            results: List[Tuple[ClientProxy, FitRes]],
+            failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+        ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+            """Aggregate fit results using bagging."""
+            if not results:
+                return None, {}
+            # Do not aggregate if there are failures and failures are not accepted
+            if not self.accept_failures and failures:
+                return None, {}
+
+            # Aggregate all the client trees
+            global_model = self.global_model
+            for _, fit_res in results:
+                update = fit_res.parameters.tensors
+                for bst in update:
+                    global_model = aggregate(global_model, bst)
+
+            self.global_model = global_model
+
+            return (
+                Parameters(tensor_type="", tensors=[cast(bytes, global_model)]),
+                {},
+            )
+
+        def aggregate_evaluate(
+            self,
+            server_round: int,
+            results: List[Tuple[ClientProxy, EvaluateRes]],
+            failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+        ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+            """Aggregate evaluation metrics using average."""
+            if not results:
+                return None, {}
+            # Do not aggregate if there are failures and failures are not accepted
+            if not self.accept_failures and failures:
+                return None, {}
+
+            # Aggregate custom metrics if aggregation fn was provided
+            metrics_aggregated = {}
+            if self.evaluate_metrics_aggregation_fn:
+                eval_metrics = [(res.num_examples, res.metrics) for _, res in results]
+                metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics)
+            elif server_round == 1:  # Only log this warning once
+                log(WARNING, "No evaluate_metrics_aggregation_fn provided")
+
+            return 0, metrics_aggregated
+
+        def evaluate(
+            self, server_round: int, parameters: Parameters
+        ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+            """Evaluate model parameters using an evaluation function."""
+            if self.evaluate_function is None:
+                # No evaluation function provided
+                return None
+            eval_res = self.evaluate_function(server_round, parameters, {})
+            if eval_res is None:
+                return None
+            loss, metrics = eval_res
+            return loss, metrics
+
+In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost trees by calling :code:`aggregate()` function:
+
+.. code-block:: python
+
+    def aggregate(
+        bst_prev_org: Optional[bytes],
+        bst_curr_org: bytes,
+    ) -> bytes:
+        """Conduct bagging aggregation for given trees."""
+        if not bst_prev_org:
+            return bst_curr_org
+
+        # Get the tree numbers
+        tree_num_prev, _ = _get_tree_nums(bst_prev_org)
+        _, paral_tree_num_curr = _get_tree_nums(bst_curr_org)
+
+        bst_prev = json.loads(bytearray(bst_prev_org))
+        bst_curr = json.loads(bytearray(bst_curr_org))
+
+        bst_prev["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+            "num_trees"
+        ] = str(tree_num_prev + paral_tree_num_curr)
+        iteration_indptr = bst_prev["learner"]["gradient_booster"]["model"][
+            "iteration_indptr"
+        ]
+        bst_prev["learner"]["gradient_booster"]["model"]["iteration_indptr"].append(
+            iteration_indptr[-1] + paral_tree_num_curr
+        )
+
+        # Aggregate new trees
+        trees_curr = bst_curr["learner"]["gradient_booster"]["model"]["trees"]
+        for tree_count in range(paral_tree_num_curr):
+            trees_curr[tree_count]["id"] = tree_num_prev + tree_count
+            bst_prev["learner"]["gradient_booster"]["model"]["trees"].append(
+                trees_curr[tree_count]
+            )
+            bst_prev["learner"]["gradient_booster"]["model"]["tree_info"].append(0)
+
+        bst_prev_bytes = bytes(json.dumps(bst_prev), "utf-8")
+
+        return bst_prev_bytes
+
+
+    def _get_tree_nums(xgb_model_org: bytes) -> Tuple[int, int]:
+        xgb_model = json.loads(bytearray(xgb_model_org))
+        # Get the number of trees
+        tree_num = int(
+            xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+                "num_trees"
+            ]
+        )
+        # Get the number of parallel trees
+        paral_tree_num = int(
+            xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+                "num_parallel_tree"
+            ]
+        )
+        return tree_num, paral_tree_num
+
+In this function, we first fetch the number of trees and the number of parallel trees for the current and previous model
+by calling :code:`_get_tree_nums`.
+Then, the fetched information will be aggregated.
+After that, the trees (containing model weights) are aggregated to generate a new tree model.
+
+After traversal of all clients' models, a new global model is generated,
+followed by the serialisation, and sending back to each client.
+
+
+Launch Federated XGBoost!
+---------------------------
+
+With both client and server ready, we can now run everything and see federated
+learning in action. FL systems usually have a server and multiple clients. We
+therefore have to start the server first:
+
+.. code-block:: shell
+
+    $ python3 server.py
+
+Once the server is running we can start the clients in different terminals.
+Open a new terminal and start the first client:
+
+.. code-block:: shell
+
+    $ python3 client.py --node-id=0
+
+Open another terminal and start the second client:
+
+.. code-block:: shell
+
+    $ python3 client.py --node-id=1
+
+Each client will have its own dataset.
+You should now see how the training does in the very first terminal (the one that started the server):
+
+.. code-block:: shell
+
+    INFO flwr 2023-11-19 18:36:33,599 | app.py:163 | Starting Flower server, config: ServerConfig(num_rounds=5, round_timeout=None)
+    INFO flwr 2023-11-19 18:36:33,629 | app.py:176 | Flower ECE: gRPC server running (5 rounds), SSL is disabled
+    INFO flwr 2023-11-19 18:36:33,629 | server.py:89 | Initializing global parameters
+    INFO flwr 2023-11-19 18:36:33,629 | server.py:276 | Requesting initial parameters from one random client
+    INFO flwr 2023-11-19 18:40:03,997 | server.py:280 | Received initial parameters from one random client
+    INFO flwr 2023-11-19 18:40:03,999 | server.py:91 | Evaluating initial parameters
+    INFO flwr 2023-11-19 18:40:04,000 | server.py:104 | FL starting
+    DEBUG flwr 2023-11-19 18:40:04,098 | server.py:222 | fit_round 1: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:09,097 | server.py:236 | fit_round 1 received 2 results and 0 failures
+    DEBUG flwr 2023-11-19 18:40:09,108 | server.py:173 | evaluate_round 1: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:09,236 | server.py:187 | evaluate_round 1 received 2 results and 0 failures
+    DEBUG flwr 2023-11-19 18:40:09,237 | server.py:222 | fit_round 2: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:09,819 | server.py:236 | fit_round 2 received 2 results and 0 failures
+    DEBUG flwr 2023-11-19 18:40:09,855 | server.py:173 | evaluate_round 2: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:10,007 | server.py:187 | evaluate_round 2 received 2 results and 0 failures
+    DEBUG flwr 2023-11-19 18:40:10,007 | server.py:222 | fit_round 3: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:10,623 | server.py:236 | fit_round 3 received 2 results and 0 failures
+    DEBUG flwr 2023-11-19 18:40:10,674 | server.py:173 | evaluate_round 3: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:10,847 | server.py:187 | evaluate_round 3 received 2 results and 0 failures
+    DEBUG flwr 2023-11-19 18:40:10,847 | server.py:222 | fit_round 4: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:11,515 | server.py:236 | fit_round 4 received 2 results and 0 failures
+    DEBUG flwr 2023-11-19 18:40:11,581 | server.py:173 | evaluate_round 4: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:11,775 | server.py:187 | evaluate_round 4 received 2 results and 0 failures
+    DEBUG flwr 2023-11-19 18:40:11,775 | server.py:222 | fit_round 5: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:12,568 | server.py:236 | fit_round 5 received 2 results and 0 failures
+    DEBUG flwr 2023-11-19 18:40:12,648 | server.py:173 | evaluate_round 5: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-19 18:40:12,862 | server.py:187 | evaluate_round 5 received 2 results and 0 failures
+    INFO flwr 2023-11-19 18:40:12,862 | server.py:153 | FL finished in 8.86196927200001
+    INFO flwr 2023-11-19 18:40:12,864 | app.py:226 | app_fit: losses_distributed [(1, 0), (2, 0), (3, 0), (4, 0), (5, 0)]
+    INFO flwr 2023-11-19 18:40:12,864 | app.py:227 | app_fit: metrics_distributed_fit {}
+    INFO flwr 2023-11-19 18:40:12,864 | app.py:228 | app_fit: metrics_distributed {'AUC': [(1, 0.76315), (2, 0.7734), (3, 0.7783), (4, 0.7824), (5, 0.78595)]}
+    INFO flwr 2023-11-19 18:40:12,864 | app.py:229 | app_fit: losses_centralized []
+    INFO flwr 2023-11-19 18:40:12,864 | app.py:230 | app_fit: metrics_centralized {}
+
+Congratulations!
+You've successfully built and run your first federated XGBoost system.
+The AUC values can be checked in :code:`metrics_distributed`.
+One can see that the average AUC increases over FL rounds.
+
+The full `source code <https://github.com/adap/flower/blob/main/examples/xgboost-quickstart/>`_ for this example can be found in :code:`examples/xgboost-quickstart`.
+
+
+Comprehensive Federated XGBoost
+---------------------------
+
+Now that you have known how federated XGBoost work with Flower, it's time to run some more comprehensive experiments by customising the experimental settings.
+In the xgboost-comprehensive example (`full code <https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive>`_),
+we provide more options to define various experimental setups, including data partitioning and centralised/distributed evaluation.
+Let's take a look!
+
+Customised data partitioning
+~~~~~~~~
+
+In :code:`dataset.py`, we have a function :code:`instantiate_partitioner` to instantiate the data partitioner
+based on the given :code:`num_partitions` and :code:`partitioner_type`.
+Currently, we provide four supported partitioner type to simulate the uniformity/non-uniformity in data quantity (uniform, linear, square, exponential).
+
+.. code-block:: python
+
+    from flwr_datasets.partitioner import (
+        IidPartitioner,
+        LinearPartitioner,
+        SquarePartitioner,
+        ExponentialPartitioner,
+    )
+
+    CORRELATION_TO_PARTITIONER = {
+        "uniform": IidPartitioner,
+        "linear": LinearPartitioner,
+        "square": SquarePartitioner,
+        "exponential": ExponentialPartitioner,
+    }
+
+
+    def instantiate_partitioner(partitioner_type: str, num_partitions: int):
+        """Initialise partitioner based on selected partitioner type and number of
+        partitions."""
+        partitioner = CORRELATION_TO_PARTITIONER[partitioner_type](
+            num_partitions=num_partitions
+        )
+        return partitioner
+
+
+Customised centralised/distributed evaluation
+~~~~~~~~
+
+To facilitate centralised evaluation, we define a function in :code:`server.py`:
+
+.. code-block:: python
+
+    def get_evaluate_fn(test_data):
+        """Return a function for centralised evaluation."""
+
+        def evaluate_fn(
+            server_round: int, parameters: Parameters, config: Dict[str, Scalar]
+        ):
+            # If at the first round, skip the evaluation
+            if server_round == 0:
+                return 0, {}
+            else:
+                bst = xgb.Booster(params=params)
+                for para in parameters.tensors:
+                    para_b = bytearray(para)
+
+                # Load global model
+                bst.load_model(para_b)
+                # Run evaluation
+                eval_results = bst.eval_set(
+                    evals=[(test_data, "valid")],
+                    iteration=bst.num_boosted_rounds() - 1,
+                )
+                auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4)
+                log(INFO, f"AUC = {auc} at round {server_round}")
+
+                return 0, {"AUC": auc}
+
+        return evaluate_fn
+
+This function returns a evaluation function which instantiates a :code:`Booster` object and loads the global model weights to it.
+The evaluation is conducted by calling :code:`eval_set()` method, and the tested AUC value is reported.
+
+As for distributed evaluation on the clients, it's same as the quick-start example by
+overriding the :code:`evaluate()` method insides the :code:`XgbClient` class in :code:`client.py`.
+
+Arguments parser
+~~~~~~~~
+
+In :code:`utils.py`, we define the arguments parsers for clients and server, allowing users to specify different experimental settings.
+Let's first see the sever side:
+
+.. code-block:: python
+
+    import argparse
+
+
+    def server_args_parser():
+        """Parse arguments to define experimental settings on server side."""
+        parser = argparse.ArgumentParser()
+
+        parser.add_argument(
+            "--pool-size", default=2, type=int, help="Number of total clients."
+        )
+        parser.add_argument(
+            "--num-rounds", default=5, type=int, help="Number of FL rounds."
+        )
+        parser.add_argument(
+            "--num-clients-per-round",
+            default=2,
+            type=int,
+            help="Number of clients participate in training each round.",
+        )
+        parser.add_argument(
+            "--num-evaluate-clients",
+            default=2,
+            type=int,
+            help="Number of clients selected for evaluation.",
+        )
+        parser.add_argument(
+            "--centralised-eval",
+            action="store_true",
+            help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
+        )
+
+        args = parser.parse_args()
+        return args
+
+This allows user to specify the number of total clients / FL rounds / participating clients / clients for evaluation,
+and evaluation fashion. Note that with :code:`--centralised-eval`, the sever will do centralised evaluation
+and all functionalities for client evaluation will be disabled.
+
+Then, the argument parser on client side:
+
+.. code-block:: python
+
+    def client_args_parser():
+        """Parse arguments to define experimental settings on client side."""
+        parser = argparse.ArgumentParser()
+
+        parser.add_argument(
+            "--num-partitions", default=10, type=int, help="Number of partitions."
+        )
+        parser.add_argument(
+            "--partitioner-type",
+            default="uniform",
+            type=str,
+            choices=["uniform", "linear", "square", "exponential"],
+            help="Partitioner types.",
+        )
+        parser.add_argument(
+            "--node-id",
+            default=0,
+            type=int,
+            help="Node ID used for the current client.",
+        )
+        parser.add_argument(
+            "--seed", default=42, type=int, help="Seed used for train/test splitting."
+        )
+        parser.add_argument(
+            "--test-fraction",
+            default=0.2,
+            type=float,
+            help="Test fraction for train/test splitting.",
+        )
+        parser.add_argument(
+            "--centralised-eval",
+            action="store_true",
+            help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
+        )
+
+        args = parser.parse_args()
+        return args
+
+This defines various options for client data partitioning.
+Besides, clients also have a option to conduct evaluation on centralised test set by setting :code:`--centralised-eval`.
+
+Example commands
+~~~~~~~~
+
+To run a centralised evaluated experiment on 5 clients with exponential distribution for 50 rounds,
+we first start the server as below:
+
+.. code-block:: shell
+
+    $ python3 server.py --pool-size=5 --num-rounds=50 --num-clients-per-round=5 --centralised-eval
+
+Then, on each client terminal, we start the clients:
+
+.. code-block:: shell
+
+    $ python3 clients.py --num-partitions=5 --partitioner-type=exponential --node-id=NODE_ID
+
+The full `source code <https://github.com/adap/flower/blob/main/examples/xgboost-comprehensive/>`_ for this comprehensive example can be found in :code:`examples/xgboost-comprehensive`.

From 78cb4171442290171d0633c08f4545274f3d19ce Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Mon, 20 Nov 2023 11:59:38 +0000
Subject: [PATCH 31/64] Update XGBoost examples (#2619)

Co-authored-by: yan-gao-GY <yan@flower.dev>
---
 doc/source/tutorial-quickstart-xgboost.rst | 69 +++++++++++-----------
 examples/xgboost-comprehensive/README.md   |  3 +-
 examples/xgboost-comprehensive/client.py   |  2 +
 examples/xgboost-quickstart/README.md      |  1 +
 examples/xgboost-quickstart/client.py      |  5 +-
 5 files changed, 44 insertions(+), 36 deletions(-)

diff --git a/doc/source/tutorial-quickstart-xgboost.rst b/doc/source/tutorial-quickstart-xgboost.rst
index 3de4e0ef81cb..8d6f78f3088a 100644
--- a/doc/source/tutorial-quickstart-xgboost.rst
+++ b/doc/source/tutorial-quickstart-xgboost.rst
@@ -88,7 +88,8 @@ Prior to local training, we require loading the HIGGS dataset from Flower Datase
 .. code-block:: python
 
     # Load (HIGGS) dataset and conduct partitioning
-    partitioner = IidPartitioner(num_partitions=2)
+    # We use a small subset (num_partitions=30) of the dataset for demonstration to speed up the data loading process.
+    partitioner = IidPartitioner(num_partitions=30)
     fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})
 
     # Load the partition for this `node_id`
@@ -544,39 +545,39 @@ You should now see how the training does in the very first terminal (the one tha
 
 .. code-block:: shell
 
-    INFO flwr 2023-11-19 18:36:33,599 | app.py:163 | Starting Flower server, config: ServerConfig(num_rounds=5, round_timeout=None)
-    INFO flwr 2023-11-19 18:36:33,629 | app.py:176 | Flower ECE: gRPC server running (5 rounds), SSL is disabled
-    INFO flwr 2023-11-19 18:36:33,629 | server.py:89 | Initializing global parameters
-    INFO flwr 2023-11-19 18:36:33,629 | server.py:276 | Requesting initial parameters from one random client
-    INFO flwr 2023-11-19 18:40:03,997 | server.py:280 | Received initial parameters from one random client
-    INFO flwr 2023-11-19 18:40:03,999 | server.py:91 | Evaluating initial parameters
-    INFO flwr 2023-11-19 18:40:04,000 | server.py:104 | FL starting
-    DEBUG flwr 2023-11-19 18:40:04,098 | server.py:222 | fit_round 1: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:09,097 | server.py:236 | fit_round 1 received 2 results and 0 failures
-    DEBUG flwr 2023-11-19 18:40:09,108 | server.py:173 | evaluate_round 1: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:09,236 | server.py:187 | evaluate_round 1 received 2 results and 0 failures
-    DEBUG flwr 2023-11-19 18:40:09,237 | server.py:222 | fit_round 2: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:09,819 | server.py:236 | fit_round 2 received 2 results and 0 failures
-    DEBUG flwr 2023-11-19 18:40:09,855 | server.py:173 | evaluate_round 2: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:10,007 | server.py:187 | evaluate_round 2 received 2 results and 0 failures
-    DEBUG flwr 2023-11-19 18:40:10,007 | server.py:222 | fit_round 3: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:10,623 | server.py:236 | fit_round 3 received 2 results and 0 failures
-    DEBUG flwr 2023-11-19 18:40:10,674 | server.py:173 | evaluate_round 3: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:10,847 | server.py:187 | evaluate_round 3 received 2 results and 0 failures
-    DEBUG flwr 2023-11-19 18:40:10,847 | server.py:222 | fit_round 4: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:11,515 | server.py:236 | fit_round 4 received 2 results and 0 failures
-    DEBUG flwr 2023-11-19 18:40:11,581 | server.py:173 | evaluate_round 4: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:11,775 | server.py:187 | evaluate_round 4 received 2 results and 0 failures
-    DEBUG flwr 2023-11-19 18:40:11,775 | server.py:222 | fit_round 5: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:12,568 | server.py:236 | fit_round 5 received 2 results and 0 failures
-    DEBUG flwr 2023-11-19 18:40:12,648 | server.py:173 | evaluate_round 5: strategy sampled 2 clients (out of 2)
-    DEBUG flwr 2023-11-19 18:40:12,862 | server.py:187 | evaluate_round 5 received 2 results and 0 failures
-    INFO flwr 2023-11-19 18:40:12,862 | server.py:153 | FL finished in 8.86196927200001
-    INFO flwr 2023-11-19 18:40:12,864 | app.py:226 | app_fit: losses_distributed [(1, 0), (2, 0), (3, 0), (4, 0), (5, 0)]
-    INFO flwr 2023-11-19 18:40:12,864 | app.py:227 | app_fit: metrics_distributed_fit {}
-    INFO flwr 2023-11-19 18:40:12,864 | app.py:228 | app_fit: metrics_distributed {'AUC': [(1, 0.76315), (2, 0.7734), (3, 0.7783), (4, 0.7824), (5, 0.78595)]}
-    INFO flwr 2023-11-19 18:40:12,864 | app.py:229 | app_fit: losses_centralized []
-    INFO flwr 2023-11-19 18:40:12,864 | app.py:230 | app_fit: metrics_centralized {}
+    INFO flwr 2023-11-20 11:21:56,454 | app.py:163 | Starting Flower server, config: ServerConfig(num_rounds=5, round_timeout=None)
+    INFO flwr 2023-11-20 11:21:56,473 | app.py:176 | Flower ECE: gRPC server running (5 rounds), SSL is disabled
+    INFO flwr 2023-11-20 11:21:56,473 | server.py:89 | Initializing global parameters
+    INFO flwr 2023-11-20 11:21:56,473 | server.py:276 | Requesting initial parameters from one random client
+    INFO flwr 2023-11-20 11:22:38,302 | server.py:280 | Received initial parameters from one random client
+    INFO flwr 2023-11-20 11:22:38,302 | server.py:91 | Evaluating initial parameters
+    INFO flwr 2023-11-20 11:22:38,302 | server.py:104 | FL starting
+    DEBUG flwr 2023-11-20 11:22:38,302 | server.py:222 | fit_round 1: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:38,636 | server.py:236 | fit_round 1 received 2 results and 0 failures
+    DEBUG flwr 2023-11-20 11:22:38,643 | server.py:173 | evaluate_round 1: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:38,653 | server.py:187 | evaluate_round 1 received 2 results and 0 failures
+    DEBUG flwr 2023-11-20 11:22:38,653 | server.py:222 | fit_round 2: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:38,721 | server.py:236 | fit_round 2 received 2 results and 0 failures
+    DEBUG flwr 2023-11-20 11:22:38,745 | server.py:173 | evaluate_round 2: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:38,756 | server.py:187 | evaluate_round 2 received 2 results and 0 failures
+    DEBUG flwr 2023-11-20 11:22:38,756 | server.py:222 | fit_round 3: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:38,831 | server.py:236 | fit_round 3 received 2 results and 0 failures
+    DEBUG flwr 2023-11-20 11:22:38,868 | server.py:173 | evaluate_round 3: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:38,881 | server.py:187 | evaluate_round 3 received 2 results and 0 failures
+    DEBUG flwr 2023-11-20 11:22:38,881 | server.py:222 | fit_round 4: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:38,960 | server.py:236 | fit_round 4 received 2 results and 0 failures
+    DEBUG flwr 2023-11-20 11:22:39,012 | server.py:173 | evaluate_round 4: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:39,026 | server.py:187 | evaluate_round 4 received 2 results and 0 failures
+    DEBUG flwr 2023-11-20 11:22:39,026 | server.py:222 | fit_round 5: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:39,111 | server.py:236 | fit_round 5 received 2 results and 0 failures
+    DEBUG flwr 2023-11-20 11:22:39,177 | server.py:173 | evaluate_round 5: strategy sampled 2 clients (out of 2)
+    DEBUG flwr 2023-11-20 11:22:39,193 | server.py:187 | evaluate_round 5 received 2 results and 0 failures
+    INFO flwr 2023-11-20 11:22:39,193 | server.py:153 | FL finished in 0.8905023969999988
+    INFO flwr 2023-11-20 11:22:39,193 | app.py:226 | app_fit: losses_distributed [(1, 0), (2, 0), (3, 0), (4, 0), (5, 0)]
+    INFO flwr 2023-11-20 11:22:39,193 | app.py:227 | app_fit: metrics_distributed_fit {}
+    INFO flwr 2023-11-20 11:22:39,193 | app.py:228 | app_fit: metrics_distributed {'AUC': [(1, 0.7572), (2, 0.7705), (3, 0.77595), (4, 0.78), (5, 0.78385)]}
+    INFO flwr 2023-11-20 11:22:39,193 | app.py:229 | app_fit: losses_centralized []
+    INFO flwr 2023-11-20 11:22:39,193 | app.py:230 | app_fit: metrics_centralized {}
 
 Congratulations!
 You've successfully built and run your first federated XGBoost system.
diff --git a/examples/xgboost-comprehensive/README.md b/examples/xgboost-comprehensive/README.md
index 783438188dab..da002a10d301 100644
--- a/examples/xgboost-comprehensive/README.md
+++ b/examples/xgboost-comprehensive/README.md
@@ -1,7 +1,8 @@
 # Flower Example using XGBoost (Comprehensive)
 
 This example demonstrates a comprehensive federated learning setup using Flower with XGBoost.
-It differs from the quickstart example in the following ways:
+We use [HIGGS](https://archive.ics.uci.edu/dataset/280/higgs) dataset to perform a binary classification task.
+It differs from the [xgboost-quickstart](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart) example in the following ways:
 
 - Arguments parsers of server and clients for hyperparameters selection.
 - Customised FL settings.
diff --git a/examples/xgboost-comprehensive/client.py b/examples/xgboost-comprehensive/client.py
index 889f5f47e0ae..bc9735a2f657 100644
--- a/examples/xgboost-comprehensive/client.py
+++ b/examples/xgboost-comprehensive/client.py
@@ -47,6 +47,7 @@
 )
 
 # Load the partition for this `node_id`
+log(INFO, "Loading partition...")
 node_id = args.node_id
 partition = fds.load_partition(idx=node_id, split="train")
 partition.set_format("numpy")
@@ -67,6 +68,7 @@
     )
 
 # Reformat data to DMatrix for xgboost
+log(INFO, "Reformatting data...")
 train_dmatrix = transform_dataset_to_dmatrix(train_data)
 valid_dmatrix = transform_dataset_to_dmatrix(valid_data)
 
diff --git a/examples/xgboost-quickstart/README.md b/examples/xgboost-quickstart/README.md
index 309f8f55b847..5174c236c668 100644
--- a/examples/xgboost-quickstart/README.md
+++ b/examples/xgboost-quickstart/README.md
@@ -1,6 +1,7 @@
 # Flower Example using XGBoost
 
 This example demonstrates how to perform EXtreme Gradient Boosting (XGBoost) within Flower using `xgboost` package.
+We use [HIGGS](https://archive.ics.uci.edu/dataset/280/higgs) dataset for this example to perform a binary classification task.
 Tree-based with bagging method is used for aggregation on the server.
 
 This project provides a minimal code example to enable you to get stated quickly. For a more comprehensive code example, take a look at [xgboost-comprehensive](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive).
diff --git a/examples/xgboost-quickstart/client.py b/examples/xgboost-quickstart/client.py
index ed85e98a3ab2..e88580197128 100644
--- a/examples/xgboost-quickstart/client.py
+++ b/examples/xgboost-quickstart/client.py
@@ -57,10 +57,12 @@ def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.
 
 
 # Load (HIGGS) dataset and conduct partitioning
-partitioner = IidPartitioner(num_partitions=2)
+# We use a small subset (num_partitions=30) of the dataset for demonstration to speed up the data loading process.
+partitioner = IidPartitioner(num_partitions=30)
 fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})
 
 # Load the partition for this `node_id`
+log(INFO, "Loading partition...")
 partition = fds.load_partition(idx=args.node_id, split="train")
 partition.set_format("numpy")
 
@@ -70,6 +72,7 @@ def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.
 )
 
 # Reformat data to DMatrix for xgboost
+log(INFO, "Reformatting data...")
 train_dmatrix = transform_dataset_to_dmatrix(train_data)
 valid_dmatrix = transform_dataset_to_dmatrix(valid_data)
 

From 10c05e26c5495e4c91ac95592ab0856f715e7199 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 20 Nov 2023 13:11:34 +0000
Subject: [PATCH 32/64] Bump pyyaml from 5.3 to 5.4 in
 /examples/quickstart-mlcube/dev (#2492)

Bumps [pyyaml](https://github.com/yaml/pyyaml) from 5.3 to 5.4.
- [Changelog](https://github.com/yaml/pyyaml/blob/main/CHANGES)
- [Commits](https://github.com/yaml/pyyaml/compare/5.3...5.4)

---
updated-dependencies:
- dependency-name: pyyaml
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Taner Topal <taner@flower.dev>
Co-authored-by: Charles Beauville <charles@flower.dev>
Co-authored-by: Daniel J. Beutel <daniel@flower.dev>
---
 examples/quickstart-mlcube/dev/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/quickstart-mlcube/dev/requirements.txt b/examples/quickstart-mlcube/dev/requirements.txt
index c39a5fa73f81..5cfa618878d2 100644
--- a/examples/quickstart-mlcube/dev/requirements.txt
+++ b/examples/quickstart-mlcube/dev/requirements.txt
@@ -1,4 +1,4 @@
-PyYAML==5.3
+PyYAML==5.4
 tensorflow==2.14.0
 tensorflow-estimator==2.14.0
 requests[security]

From 10bfc33df73ec461de4ed0f4a99d09fb645a1555 Mon Sep 17 00:00:00 2001
From: Gabriel Mota <63614154+gfmota@users.noreply.github.com>
Date: Mon, 20 Nov 2023 19:12:06 -0300
Subject: [PATCH 33/64] Fix FedXgbNnAvg strategy doc to reference the right
 paper (#2542)

Signed-off-by: gfmota <gabrielfmota134@gmail.com>
---
 src/py/flwr/server/strategy/fedxgb_nn_avg.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/py/flwr/server/strategy/fedxgb_nn_avg.py b/src/py/flwr/server/strategy/fedxgb_nn_avg.py
index 020e0ef71267..f300633d0d9f 100644
--- a/src/py/flwr/server/strategy/fedxgb_nn_avg.py
+++ b/src/py/flwr/server/strategy/fedxgb_nn_avg.py
@@ -17,7 +17,7 @@
 Strategy in the horizontal setting based on building Neural Network and averaging on
 prediction outcomes.
 
-Paper: Coming
+Paper: arxiv.org/abs/2304.07537
 """
 
 
@@ -35,6 +35,13 @@
 class FedXgbNnAvg(FedAvg):
     """Configurable FedXgbNnAvg strategy implementation."""
 
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        """Federated XGBoost [Ma et al., 2023] strategy.
+
+        Implementation based on https://arxiv.org/abs/2304.07537.
+        """
+        super().__init__(*args, **kwargs)
+
     def __repr__(self) -> str:
         """Compute a string representation of the strategy."""
         rep = f"FedXgbNnAvg(accept_failures={self.accept_failures})"

From 8bcc4bdaa80f065ce5a95f19326c9a7bc0ef74f7 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.dev>
Date: Tue, 21 Nov 2023 12:16:47 +0100
Subject: [PATCH 34/64] Introduce experimental feature warning (#2622)

---
 .../flwr/client/grpc_rere_client/connection.py   | 14 ++++----------
 src/py/flwr/common/logger.py                     | 16 +++++++++++++++-
 2 files changed, 19 insertions(+), 11 deletions(-)

diff --git a/src/py/flwr/client/grpc_rere_client/connection.py b/src/py/flwr/client/grpc_rere_client/connection.py
index b69228826e13..424e413dc484 100644
--- a/src/py/flwr/client/grpc_rere_client/connection.py
+++ b/src/py/flwr/client/grpc_rere_client/connection.py
@@ -16,7 +16,7 @@
 
 
 from contextlib import contextmanager
-from logging import DEBUG, ERROR, WARN
+from logging import DEBUG, ERROR
 from pathlib import Path
 from typing import Callable, Dict, Iterator, Optional, Tuple, Union, cast
 
@@ -28,7 +28,7 @@
 )
 from flwr.common import GRPC_MAX_MESSAGE_LENGTH
 from flwr.common.grpc import create_channel
-from flwr.common.logger import log
+from flwr.common.logger import log, warn_experimental_feature
 from flwr.proto.fleet_pb2 import (
     CreateNodeRequest,
     DeleteNodeRequest,
@@ -88,6 +88,8 @@ def grpc_request_response(
     create_node : Optional[Callable]
     delete_node : Optional[Callable]
     """
+    warn_experimental_feature("`grpc-rere`")
+
     if isinstance(root_certificates, str):
         root_certificates = Path(root_certificates).read_bytes()
 
@@ -99,14 +101,6 @@ def grpc_request_response(
     channel.subscribe(on_channel_state_change)
     stub = FleetStub(channel)
 
-    log(
-        WARN,
-        """
-        EXPERIMENTAL: `grpc-rere` is an experimental transport layer, it might change
-        considerably in future versions of Flower
-        """,
-    )
-
     # Necessary state to link TaskRes to TaskIns
     state: Dict[str, Optional[TaskIns]] = {KEY_TASK_INS: None}
 
diff --git a/src/py/flwr/common/logger.py b/src/py/flwr/common/logger.py
index e543d6565878..29d1562a86d3 100644
--- a/src/py/flwr/common/logger.py
+++ b/src/py/flwr/common/logger.py
@@ -16,7 +16,7 @@
 
 
 import logging
-from logging import LogRecord
+from logging import WARN, LogRecord
 from logging.handlers import HTTPHandler
 from typing import Any, Dict, Optional, Tuple
 
@@ -97,3 +97,17 @@ def configure(
 
 logger = logging.getLogger(LOGGER_NAME)  # pylint: disable=invalid-name
 log = logger.log  # pylint: disable=invalid-name
+
+
+def warn_experimental_feature(name: str) -> None:
+    """Warn the user when they use an experimental feature."""
+    log(
+        WARN,
+        """
+        EXPERIMENTAL FEATURE: %s
+
+        This is an experimental feature. It could change significantly or be removed
+        entirely in future versions of Flower.
+        """,
+        name,
+    )

From 1ad038b5de27e250c5773b212395e27c0e9d7833 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Tue, 21 Nov 2023 15:56:31 +0100
Subject: [PATCH 35/64] Fix wrong status int value in Android SDK (#2623)

---
 .../flwr/src/main/java/dev/flower/android/Typing.kt    | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/kotlin/flwr/src/main/java/dev/flower/android/Typing.kt b/src/kotlin/flwr/src/main/java/dev/flower/android/Typing.kt
index a88af0e28974..6db7ecd36987 100644
--- a/src/kotlin/flwr/src/main/java/dev/flower/android/Typing.kt
+++ b/src/kotlin/flwr/src/main/java/dev/flower/android/Typing.kt
@@ -23,11 +23,11 @@ typealias Properties = Map<String, Scalar>
  * The `Code` class defines client status codes used in the application.
  */
 enum class Code(val value: Int) {
-    OK(1),
-    GET_PROPERTIES_NOT_IMPLEMENTED(2),
-    GET_PARAMETERS_NOT_IMPLEMENTED(3),
-    FIT_NOT_IMPLEMENTED(4),
-    EVALUATE_NOT_IMPLEMENTED(5);
+    OK(0),
+    GET_PROPERTIES_NOT_IMPLEMENTED(1),
+    GET_PARAMETERS_NOT_IMPLEMENTED(2),
+    FIT_NOT_IMPLEMENTED(3),
+    EVALUATE_NOT_IMPLEMENTED(4);
 
     companion object {
         fun fromInt(value: Int): Code = values().first { it.value == value }

From 821d843278e60c55acdfb3574de8958c26f7a644 Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Tue, 21 Nov 2023 21:48:36 +0000
Subject: [PATCH 36/64] XGBoost -- Fix node_id issue to fit FDSv0.0.2 (#2624)

Co-authored-by: yan-gao-GY <yan@flower.dev>
---
 doc/source/tutorial-quickstart-xgboost.rst      | 2 +-
 examples/xgboost-comprehensive/client.py        | 6 ++++--
 examples/xgboost-comprehensive/pyproject.toml   | 2 +-
 examples/xgboost-comprehensive/requirements.txt | 2 +-
 examples/xgboost-quickstart/client.py           | 2 +-
 examples/xgboost-quickstart/pyproject.toml      | 2 +-
 examples/xgboost-quickstart/requirements.txt    | 2 +-
 7 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/doc/source/tutorial-quickstart-xgboost.rst b/doc/source/tutorial-quickstart-xgboost.rst
index 8d6f78f3088a..111920d5602b 100644
--- a/doc/source/tutorial-quickstart-xgboost.rst
+++ b/doc/source/tutorial-quickstart-xgboost.rst
@@ -93,7 +93,7 @@ Prior to local training, we require loading the HIGGS dataset from Flower Datase
     fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})
 
     # Load the partition for this `node_id`
-    partition = fds.load_partition(idx=args.node_id, split="train")
+    partition = fds.load_partition(node_id=args.node_id, split="train")
     partition.set_format("numpy")
 
 In this example, we split the dataset into two partitions with uniform distribution (:code:`IidPartitioner(num_partitions=2)`).
diff --git a/examples/xgboost-comprehensive/client.py b/examples/xgboost-comprehensive/client.py
index bc9735a2f657..a37edac32648 100644
--- a/examples/xgboost-comprehensive/client.py
+++ b/examples/xgboost-comprehensive/client.py
@@ -43,13 +43,15 @@
     partitioner_type=partitioner_type, num_partitions=num_partitions
 )
 fds = FederatedDataset(
-    dataset="jxie/higgs", partitioners={"train": partitioner}, resplitter=resplit
+    dataset="jxie/higgs",
+    partitioners={"train": partitioner},
+    resplitter=resplit,
 )
 
 # Load the partition for this `node_id`
 log(INFO, "Loading partition...")
 node_id = args.node_id
-partition = fds.load_partition(idx=node_id, split="train")
+partition = fds.load_partition(node_id=node_id, split="train")
 partition.set_format("numpy")
 
 if args.centralised_eval:
diff --git a/examples/xgboost-comprehensive/pyproject.toml b/examples/xgboost-comprehensive/pyproject.toml
index 5414b5122154..bbfbb4134b8d 100644
--- a/examples/xgboost-comprehensive/pyproject.toml
+++ b/examples/xgboost-comprehensive/pyproject.toml
@@ -10,6 +10,6 @@ authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr = ">=1.0,<2.0"
+flwr-nightly = ">=1.0,<2.0"
 flwr-datasets = ">=0.0.2,<1.0.0"
 xgboost = ">=2.0.0,<3.0.0"
diff --git a/examples/xgboost-comprehensive/requirements.txt b/examples/xgboost-comprehensive/requirements.txt
index c6b9c1a67894..c37ac2b6ad6d 100644
--- a/examples/xgboost-comprehensive/requirements.txt
+++ b/examples/xgboost-comprehensive/requirements.txt
@@ -1,3 +1,3 @@
-flwr>=1.0, <2.0
+flwr-nightly>=1.0, <2.0
 flwr-datasets>=0.0.2, <1.0.0
 xgboost>=2.0.0, <3.0.0
diff --git a/examples/xgboost-quickstart/client.py b/examples/xgboost-quickstart/client.py
index e88580197128..b5eab59ba14d 100644
--- a/examples/xgboost-quickstart/client.py
+++ b/examples/xgboost-quickstart/client.py
@@ -63,7 +63,7 @@ def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.
 
 # Load the partition for this `node_id`
 log(INFO, "Loading partition...")
-partition = fds.load_partition(idx=args.node_id, split="train")
+partition = fds.load_partition(node_id=args.node_id, split="train")
 partition.set_format("numpy")
 
 # Train/test splitting
diff --git a/examples/xgboost-quickstart/pyproject.toml b/examples/xgboost-quickstart/pyproject.toml
index 74256846c693..d82535311e58 100644
--- a/examples/xgboost-quickstart/pyproject.toml
+++ b/examples/xgboost-quickstart/pyproject.toml
@@ -10,6 +10,6 @@ authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr = ">=1.0,<2.0"
+flwr-nightly = ">=1.0,<2.0"
 flwr-datasets = ">=0.0.1,<1.0.0"
 xgboost = ">=2.0.0,<3.0.0"
diff --git a/examples/xgboost-quickstart/requirements.txt b/examples/xgboost-quickstart/requirements.txt
index 9596a8d6cd02..aefd74097582 100644
--- a/examples/xgboost-quickstart/requirements.txt
+++ b/examples/xgboost-quickstart/requirements.txt
@@ -1,3 +1,3 @@
-flwr>=1.0, <2.0
+flwr-nightly>=1.0, <2.0
 flwr-datasets>=0.0.1, <1.0.0
 xgboost>=2.0.0, <3.0.0

From 1f9fa755d005f1df4aa01366ca482385b9520de8 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.dev>
Date: Thu, 23 Nov 2023 09:15:15 +0100
Subject: [PATCH 37/64] Decouple client callable (#2393)

---
 examples/mt-pytorch-callable/README.md        |  49 +++++++
 examples/mt-pytorch-callable/client.py        | 123 ++++++++++++++++
 examples/mt-pytorch-callable/driver.py        |  25 ++++
 examples/mt-pytorch-callable/pyproject.toml   |  16 ++
 examples/mt-pytorch-callable/requirements.txt |   4 +
 examples/mt-pytorch-callable/run.sh           |  20 +++
 examples/mt-pytorch-callable/server.py        |  25 ++++
 src/py/flwr/__init__.py                       |   3 +-
 src/py/flwr/client/app.py                     |  84 ++++++++---
 src/py/flwr/client/flower.py                  | 138 ++++++++++++++++++
 src/py/flwr/flower/__init__.py                |  26 ++++
 11 files changed, 495 insertions(+), 18 deletions(-)
 create mode 100644 examples/mt-pytorch-callable/README.md
 create mode 100644 examples/mt-pytorch-callable/client.py
 create mode 100644 examples/mt-pytorch-callable/driver.py
 create mode 100644 examples/mt-pytorch-callable/pyproject.toml
 create mode 100644 examples/mt-pytorch-callable/requirements.txt
 create mode 100755 examples/mt-pytorch-callable/run.sh
 create mode 100644 examples/mt-pytorch-callable/server.py
 create mode 100644 src/py/flwr/client/flower.py
 create mode 100644 src/py/flwr/flower/__init__.py

diff --git a/examples/mt-pytorch-callable/README.md b/examples/mt-pytorch-callable/README.md
new file mode 100644
index 000000000000..65ef000c26f2
--- /dev/null
+++ b/examples/mt-pytorch-callable/README.md
@@ -0,0 +1,49 @@
+# Deploy 🧪
+
+🧪 = this page covers experimental features that might change in future versions of Flower
+
+This how-to guide describes the deployment of a long-running Flower server.
+
+## Preconditions
+
+Let's assume the following project structure:
+
+```bash
+$ tree .
+.
+└── client.py
+├── driver.py
+├── requirements.txt
+```
+
+## Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+## Start the long-running Flower server
+
+```bash
+flower-server --insecure
+```
+
+## Start the long-running Flower client
+
+In a new terminal window, start the first long-running Flower client:
+
+```bash
+flower-client --callable client:flower
+```
+
+In yet another new terminal window, start the second long-running Flower client:
+
+```bash
+flower-client --callable client:flower
+```
+
+## Start the Driver script
+
+```bash
+python driver.py
+```
diff --git a/examples/mt-pytorch-callable/client.py b/examples/mt-pytorch-callable/client.py
new file mode 100644
index 000000000000..6f9747784ae0
--- /dev/null
+++ b/examples/mt-pytorch-callable/client.py
@@ -0,0 +1,123 @@
+import warnings
+from collections import OrderedDict
+
+import flwr as fl
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torchvision.datasets import CIFAR10
+from torchvision.transforms import Compose, Normalize, ToTensor
+from tqdm import tqdm
+
+
+# #############################################################################
+# 1. Regular PyTorch pipeline: nn.Module, train, test, and DataLoader
+# #############################################################################
+
+warnings.filterwarnings("ignore", category=UserWarning)
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+class Net(nn.Module):
+    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
+
+    def __init__(self) -> None:
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return self.fc3(x)
+
+
+def train(net, trainloader, epochs):
+    """Train the model on the training set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+    for _ in range(epochs):
+        for images, labels in tqdm(trainloader):
+            optimizer.zero_grad()
+            criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
+            optimizer.step()
+
+
+def test(net, testloader):
+    """Validate the model on the test set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, loss = 0, 0.0
+    with torch.no_grad():
+        for images, labels in tqdm(testloader):
+            outputs = net(images.to(DEVICE))
+            labels = labels.to(DEVICE)
+            loss += criterion(outputs, labels).item()
+            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
+    accuracy = correct / len(testloader.dataset)
+    return loss, accuracy
+
+
+def load_data():
+    """Load CIFAR-10 (training and test set)."""
+    trf = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+    trainset = CIFAR10("./data", train=True, download=True, transform=trf)
+    testset = CIFAR10("./data", train=False, download=True, transform=trf)
+    return DataLoader(trainset, batch_size=32, shuffle=True), DataLoader(testset)
+
+
+# #############################################################################
+# 2. Federation of the pipeline with Flower
+# #############################################################################
+
+# Load model and data (simple CNN, CIFAR-10)
+net = Net().to(DEVICE)
+trainloader, testloader = load_data()
+
+
+# Define Flower client
+class FlowerClient(fl.client.NumPyClient):
+    def get_parameters(self, config):
+        return [val.cpu().numpy() for _, val in net.state_dict().items()]
+
+    def set_parameters(self, parameters):
+        params_dict = zip(net.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
+        net.load_state_dict(state_dict, strict=True)
+
+    def fit(self, parameters, config):
+        self.set_parameters(parameters)
+        train(net, trainloader, epochs=1)
+        return self.get_parameters(config={}), len(trainloader.dataset), {}
+
+    def evaluate(self, parameters, config):
+        self.set_parameters(parameters)
+        loss, accuracy = test(net, testloader)
+        return loss, len(testloader.dataset), {"accuracy": accuracy}
+
+
+def client_fn(cid: str):
+    """."""
+    return FlowerClient().to_client()
+
+
+# To run this: `flower-client --callable client:flower`
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
+
+
+if __name__ == "__main__":
+    # Start Flower client
+    fl.client.start_client(
+        server_address="0.0.0.0:9092",
+        client=FlowerClient().to_client(),
+        transport="grpc-rere",
+    )
diff --git a/examples/mt-pytorch-callable/driver.py b/examples/mt-pytorch-callable/driver.py
new file mode 100644
index 000000000000..1248672b6813
--- /dev/null
+++ b/examples/mt-pytorch-callable/driver.py
@@ -0,0 +1,25 @@
+from typing import List, Tuple
+
+import flwr as fl
+from flwr.common import Metrics
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    # Multiply accuracy of each client by number of examples used
+    accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {"accuracy": sum(accuracies) / sum(examples)}
+
+
+# Define strategy
+strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
+
+# Start Flower driver
+fl.driver.start_driver(
+    server_address="0.0.0.0:9091",
+    config=fl.server.ServerConfig(num_rounds=3),
+    strategy=strategy,
+)
diff --git a/examples/mt-pytorch-callable/pyproject.toml b/examples/mt-pytorch-callable/pyproject.toml
new file mode 100644
index 000000000000..0d1a91836006
--- /dev/null
+++ b/examples/mt-pytorch-callable/pyproject.toml
@@ -0,0 +1,16 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "quickstart-pytorch"
+version = "0.1.0"
+description = "PyTorch Federated Learning Quickstart with Flower"
+authors = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = { path = "../../", develop = true, extras = ["simulation", "rest"] }
+torch = "1.13.1"
+torchvision = "0.14.1"
+tqdm = "4.65.0"
diff --git a/examples/mt-pytorch-callable/requirements.txt b/examples/mt-pytorch-callable/requirements.txt
new file mode 100644
index 000000000000..797ca6db6244
--- /dev/null
+++ b/examples/mt-pytorch-callable/requirements.txt
@@ -0,0 +1,4 @@
+flwr>=1.0, <2.0
+torch==1.13.1
+torchvision==0.14.1
+tqdm==4.65.0
diff --git a/examples/mt-pytorch-callable/run.sh b/examples/mt-pytorch-callable/run.sh
new file mode 100755
index 000000000000..d2bf34f834b1
--- /dev/null
+++ b/examples/mt-pytorch-callable/run.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
+
+# Download the CIFAR-10 dataset
+python -c "from torchvision.datasets import CIFAR10; CIFAR10('./data', download=True)"
+
+echo "Starting server"
+python server.py &
+sleep 3  # Sleep for 3s to give the server enough time to start
+
+for i in `seq 0 1`; do
+    echo "Starting client $i"
+    python client.py &
+done
+
+# Enable CTRL+C to stop all background processes
+trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+# Wait for all background processes to complete
+wait
diff --git a/examples/mt-pytorch-callable/server.py b/examples/mt-pytorch-callable/server.py
new file mode 100644
index 000000000000..fe691a88aba0
--- /dev/null
+++ b/examples/mt-pytorch-callable/server.py
@@ -0,0 +1,25 @@
+from typing import List, Tuple
+
+import flwr as fl
+from flwr.common import Metrics
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    # Multiply accuracy of each client by number of examples used
+    accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {"accuracy": sum(accuracies) / sum(examples)}
+
+
+# Define strategy
+strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
+
+# Start Flower server
+fl.server.start_server(
+    server_address="0.0.0.0:8080",
+    config=fl.server.ServerConfig(num_rounds=3),
+    strategy=strategy,
+)
diff --git a/src/py/flwr/__init__.py b/src/py/flwr/__init__.py
index d3cbf00747a4..e05799280339 100644
--- a/src/py/flwr/__init__.py
+++ b/src/py/flwr/__init__.py
@@ -17,12 +17,13 @@
 
 from flwr.common.version import package_version as _package_version
 
-from . import client, common, driver, server, simulation
+from . import client, common, driver, flower, server, simulation
 
 __all__ = [
     "client",
     "common",
     "driver",
+    "flower",
     "server",
     "simulation",
 ]
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 0013b74c631c..b39dbbfc33c0 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -22,6 +22,7 @@
 from typing import Callable, ContextManager, Optional, Tuple, Union
 
 from flwr.client.client import Client
+from flwr.client.flower import Bwd, Flower, Fwd
 from flwr.client.typing import ClientFn
 from flwr.common import GRPC_MAX_MESSAGE_LENGTH, EventType, event
 from flwr.common.address import parse_address
@@ -32,13 +33,15 @@
     TRANSPORT_TYPE_REST,
     TRANSPORT_TYPES,
 )
-from flwr.common.logger import log
+from flwr.common.logger import log, warn_experimental_feature
 from flwr.proto.task_pb2 import TaskIns, TaskRes
 
+from .flower import load_callable
 from .grpc_client.connection import grpc_connection
 from .grpc_rere_client.connection import grpc_request_response
-from .message_handler.message_handler import handle, handle_control_message
+from .message_handler.message_handler import handle_control_message
 from .numpy_client import NumPyClient
+from .workload_state import WorkloadState
 
 
 def run_client() -> None:
@@ -48,6 +51,22 @@ def run_client() -> None:
     args = _parse_args_client().parse_args()
 
     print(args.server)
+    print(args.callable_dir)
+    print(args.callable)
+
+    callable_dir = args.callable_dir
+    if callable_dir is not None:
+        sys.path.insert(0, callable_dir)
+
+    def _load() -> Flower:
+        flower: Flower = load_callable(args.callable)
+        return flower
+
+    return start_client(
+        server_address=args.server,
+        load_callable_fn=_load,
+        transport="grpc-rere",  # Only
+    )
 
 
 def _parse_args_client() -> argparse.ArgumentParser:
@@ -58,8 +77,18 @@ def _parse_args_client() -> argparse.ArgumentParser:
 
     parser.add_argument(
         "--server",
-        help="Server address",
         default="0.0.0.0:9092",
+        help="Server address",
+    )
+    parser.add_argument(
+        "--callable",
+        help="For example: `client:flower` or `project.package.module:wrapper.flower`",
+    )
+    parser.add_argument(
+        "--callable-dir",
+        default="",
+        help="Add specified directory to the PYTHONPATH and load callable from there."
+        " Default: current working directory.",
     )
 
     return parser
@@ -84,6 +113,7 @@ def _check_actionable_client(
 def start_client(
     *,
     server_address: str,
+    load_callable_fn: Optional[Callable[[], Flower]] = None,
     client_fn: Optional[ClientFn] = None,
     client: Optional[Client] = None,
     grpc_max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,
@@ -98,6 +128,8 @@ def start_client(
         The IPv4 or IPv6 address of the server. If the Flower
         server runs on the same machine on port 8080, then `server_address`
         would be `"[::]:8080"`.
+    load_callable_fn : Optional[Callable[[], Flower]] (default: None)
+        ...
     client_fn : Optional[ClientFn]
         A callable that instantiates a Client. (default: None)
     client : Optional[flwr.client.Client]
@@ -146,20 +178,31 @@ class `flwr.client.Client` (default: None)
     """
     event(EventType.START_CLIENT_ENTER)
 
-    _check_actionable_client(client, client_fn)
+    if load_callable_fn is None:
+        _check_actionable_client(client, client_fn)
 
-    if client_fn is None:
-        # Wrap `Client` instance in `client_fn`
-        def single_client_factory(
-            cid: str,  # pylint: disable=unused-argument
-        ) -> Client:
-            if client is None:  # Added this to keep mypy happy
-                raise Exception(
-                    "Both `client_fn` and `client` are `None`, but one is required"
-                )
-            return client  # Always return the same instance
+        if client_fn is None:
+            # Wrap `Client` instance in `client_fn`
+            def single_client_factory(
+                cid: str,  # pylint: disable=unused-argument
+            ) -> Client:
+                if client is None:  # Added this to keep mypy happy
+                    raise Exception(
+                        "Both `client_fn` and `client` are `None`, but one is required"
+                    )
+                return client  # Always return the same instance
+
+            client_fn = single_client_factory
+
+        def _load_app() -> Flower:
+            return Flower(client_fn=client_fn)
 
-        client_fn = single_client_factory
+        load_callable_fn = _load_app
+    else:
+        warn_experimental_feature("`load_callable_fn`")
+
+    # At this point, only `load_callable_fn` should be used
+    # Both `client` and `client_fn` must not be used directly
 
     # Initialize connection context manager
     connection, address = _init_connection(transport, server_address)
@@ -190,11 +233,18 @@ def single_client_factory(
                     send(task_res)
                     break
 
+                # Load app
+                app: Flower = load_callable_fn()
+
                 # Handle task message
-                task_res = handle(client_fn, task_ins)
+                fwd_msg: Fwd = Fwd(
+                    task_ins=task_ins,
+                    state=WorkloadState(state={}),
+                )
+                bwd_msg: Bwd = app(fwd=fwd_msg)
 
                 # Send
-                send(task_res)
+                send(bwd_msg.task_res)
 
             # Unregister node
             if delete_node is not None:
diff --git a/src/py/flwr/client/flower.py b/src/py/flwr/client/flower.py
new file mode 100644
index 000000000000..9eeb41887e24
--- /dev/null
+++ b/src/py/flwr/client/flower.py
@@ -0,0 +1,138 @@
+# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower callable."""
+
+
+import importlib
+from dataclasses import dataclass
+from typing import Callable, cast
+
+from flwr.client.message_handler.message_handler import handle
+from flwr.client.typing import ClientFn
+from flwr.client.workload_state import WorkloadState
+from flwr.proto.task_pb2 import TaskIns, TaskRes
+
+
+@dataclass
+class Fwd:
+    """."""
+
+    task_ins: TaskIns
+    state: WorkloadState
+
+
+@dataclass
+class Bwd:
+    """."""
+
+    task_res: TaskRes
+    state: WorkloadState
+
+
+FlowerCallable = Callable[[Fwd], Bwd]
+
+
+class Flower:
+    """Flower callable.
+
+    Examples
+    --------
+    Assuming a typical client implementation in `FlowerClient`, you can wrap it in a
+    Flower callable as follows:
+
+    >>> class FlowerClient(NumPyClient):
+    >>>     # ...
+    >>>
+    >>> def client_fn(cid):
+    >>>    return FlowerClient().to_client()
+    >>>
+    >>> flower = Flower(client_fn)
+
+    If the above code is in a Python module called `client`, it can be started as
+    follows:
+
+    >>> flower-client --callable client:flower
+
+    In this `client:flower` example, `client` refers to the Python module in which the
+    previous code lives in. `flower` refers to the global attribute `flower` that points
+    to an object of type `Flower` (a Flower callable).
+    """
+
+    def __init__(
+        self,
+        client_fn: ClientFn,  # Only for backward compatibility
+    ) -> None:
+        self.client_fn = client_fn
+
+    def __call__(self, fwd: Fwd) -> Bwd:
+        """."""
+        # Execute the task
+        task_res = handle(
+            client_fn=self.client_fn,
+            task_ins=fwd.task_ins,
+        )
+        return Bwd(
+            task_res=task_res,
+            state=WorkloadState(state={}),
+        )
+
+
+class LoadCallableError(Exception):
+    """."""
+
+
+def load_callable(module_attribute_str: str) -> Flower:
+    """Load the `Flower` object specified in a module attribute string.
+
+    The module/attribute string should have the form <module>:<attribute>. Valid
+    examples include `client:flower` and `project.package.module:wrapper.flower`. It
+    must refer to a module on the PYTHONPATH, the module needs to have the specified
+    attribute, and the attribute must be of type `Flower`.
+    """
+    module_str, _, attributes_str = module_attribute_str.partition(":")
+    if not module_str:
+        raise LoadCallableError(
+            f"Missing module in {module_attribute_str}",
+        ) from None
+    if not attributes_str:
+        raise LoadCallableError(
+            f"Missing attribute in {module_attribute_str}",
+        ) from None
+
+    # Load module
+    try:
+        module = importlib.import_module(module_str)
+    except ModuleNotFoundError:
+        raise LoadCallableError(
+            f"Unable to load module {module_str}",
+        ) from None
+
+    # Recursively load attribute
+    attribute = module
+    try:
+        for attribute_str in attributes_str.split("."):
+            attribute = getattr(attribute, attribute_str)
+    except AttributeError:
+        raise LoadCallableError(
+            f"Unable to load attribute {attributes_str} from module {module_str}",
+        ) from None
+
+    # Check type
+    if not isinstance(attribute, Flower):
+        raise LoadCallableError(
+            f"Attribute {attributes_str} is not of type {Flower}",
+        ) from None
+
+    return cast(Flower, attribute)
diff --git a/src/py/flwr/flower/__init__.py b/src/py/flwr/flower/__init__.py
new file mode 100644
index 000000000000..090c78062d02
--- /dev/null
+++ b/src/py/flwr/flower/__init__.py
@@ -0,0 +1,26 @@
+# Copyright 2020 Adap GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower callable package."""
+
+
+from flwr.client.flower import Bwd as Bwd
+from flwr.client.flower import Flower as Flower
+from flwr.client.flower import Fwd as Fwd
+
+__all__ = [
+    "Flower",
+    "Fwd",
+    "Bwd",
+]

From 18c838eac2496708dcc45d6865ec2c0721bb7f24 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 23 Nov 2023 11:08:46 +0100
Subject: [PATCH 38/64] Automate FDS reference doc generation (#2562)

---
 datasets/dev/build-flwr-datasets-docs.sh      | 30 +++++++++
 .../source/_templates/autosummary/class.rst   | 33 ++++++++++
 .../source/_templates/autosummary/module.rst  | 66 +++++++++++++++++++
 datasets/doc/source/conf.py                   | 34 ++++++++++
 datasets/doc/source/index.rst                 | 10 ++-
 datasets/doc/source/ref-api-flwr-datasets.rst | 27 --------
 dev/build-docs.sh                             |  3 +-
 7 files changed, 171 insertions(+), 32 deletions(-)
 create mode 100755 datasets/dev/build-flwr-datasets-docs.sh
 create mode 100644 datasets/doc/source/_templates/autosummary/class.rst
 create mode 100644 datasets/doc/source/_templates/autosummary/module.rst
 delete mode 100644 datasets/doc/source/ref-api-flwr-datasets.rst

diff --git a/datasets/dev/build-flwr-datasets-docs.sh b/datasets/dev/build-flwr-datasets-docs.sh
new file mode 100755
index 000000000000..dc3cd979d5c8
--- /dev/null
+++ b/datasets/dev/build-flwr-datasets-docs.sh
@@ -0,0 +1,30 @@
+#!/bin/bash
+# Generating the docs, rename and move the files such that the meet the convention used in Flower.
+# Note that it involves two runs of sphinx-build that are necessary.
+# The first run generates the .rst files (and the html files that are discarded)
+# The second time it is run after the files are renamed and moved to the correct place. It generates the final htmls.
+
+set -e
+
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )"  >/dev/null 2>&1 && pwd )"/../doc
+
+# Remove the old docs from source/ref-api
+REF_API_DIR="source/ref-api"
+if [[ -d "$REF_API_DIR" ]]; then
+
+  echo "Removing ${REF_API_DIR}"
+  rm -r ${REF_API_DIR}
+fi
+
+# Remove the old html files
+if [[ -d build ]]; then
+  echo "Removing ./build"
+  rm -r build
+fi
+
+# Docs generation: Generate new rst files
+# It starts at the __init__ in the main directory and recursively generated the documentation for the
+# specified classes/modules/packages specified in __all__.
+# Note if a package cannot be reach via the recursive traversal, even if it has __all__, it won't be documented.
+echo "Generating the docs based on only the functionality given in the __all__."
+sphinx-build -M html source build
diff --git a/datasets/doc/source/_templates/autosummary/class.rst b/datasets/doc/source/_templates/autosummary/class.rst
new file mode 100644
index 000000000000..b4b35789bc6f
--- /dev/null
+++ b/datasets/doc/source/_templates/autosummary/class.rst
@@ -0,0 +1,33 @@
+{{ name | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :members:
+   :show-inheritance:
+   :inherited-members:
+
+   {% block methods %}
+
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+   {% for item in methods %}
+      {% if item != "__init__" %}
+         ~{{ name }}.{{ item }}
+      {% endif %}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
diff --git a/datasets/doc/source/_templates/autosummary/module.rst b/datasets/doc/source/_templates/autosummary/module.rst
new file mode 100644
index 000000000000..571db198d27c
--- /dev/null
+++ b/datasets/doc/source/_templates/autosummary/module.rst
@@ -0,0 +1,66 @@
+{{ name | escape | underline}}
+
+.. automodule:: {{ fullname }}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:
+   {% for item in attributes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block functions %}
+   {% if functions %}
+   .. rubric:: {{ _('Functions') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in functions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block classes %}
+   {% if classes %}
+   .. rubric:: {{ _('Classes') }}
+
+   .. autosummary::
+      :toctree:
+      :template: autosummary/class.rst
+   {% for item in classes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block exceptions %}
+   {% if exceptions %}
+   .. rubric:: {{ _('Exceptions') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+{% block modules %}
+{% if modules %}
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: autosummary/module.rst
+   :recursive:
+{% for item in modules %}
+   {{ item }}
+{%- endfor %}
+{% endif %}
+{% endblock %}
diff --git a/datasets/doc/source/conf.py b/datasets/doc/source/conf.py
index 4fccaf0ef084..32baa6dd1471 100644
--- a/datasets/doc/source/conf.py
+++ b/datasets/doc/source/conf.py
@@ -61,8 +61,42 @@
     "nbsphinx",
 ]
 
+# Generate .rst files
 autosummary_generate = True
 
+# Document ONLY the objects from __all__ (present in __init__ files).
+# It will be done recursively starting from flwr_dataset.__init__
+# It's controlled in the index.rst file.
+autosummary_ignore_module_all = False
+
+# Each class and function docs start with the path to it
+# Make the flwr_datasets.federated_dataset.FederatedDataset appear as FederatedDataset
+# The full name is still at the top of the page
+add_module_names = False
+
+def find_test_modules(package_path):
+    """Go through the python files and exclude every *_test.py file."""
+    full_path_modules = []
+    for root, dirs, files in os.walk(package_path):
+        for file in files:
+            if file.endswith('_test.py'):
+                # Construct the module path relative to the package directory
+                full_path = os.path.join(root, file)
+                relative_path = os.path.relpath(full_path, package_path)
+                # Convert file path to dotted module path
+                module_path = os.path.splitext(relative_path)[0].replace(os.sep, '.')
+                full_path_modules.append(module_path)
+    modules = []
+    for full_path_module in full_path_modules:
+        parts = full_path_module.split('.')
+        for i in range(len(parts)):
+            modules.append('.'.join(parts[i:]))
+    return modules
+
+# Stop from documenting the *_test.py files.
+# That's the only way to do that in autosummary (make the modules as mock_imports).
+autodoc_mock_imports = find_test_modules(os.path.abspath("../../"))
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
diff --git a/datasets/doc/source/index.rst b/datasets/doc/source/index.rst
index 7b19624b341a..ae7e7259f504 100644
--- a/datasets/doc/source/index.rst
+++ b/datasets/doc/source/index.rst
@@ -38,11 +38,15 @@ References
 
 Information-oriented API reference and other reference material.
 
-.. toctree::
-   :maxdepth: 2
+.. autosummary::
+   :toctree: ref-api
+   :template: autosummary/module.rst
    :caption: API reference
+   :recursive:
+
+      flwr_datasets
+
 
-   ref-api-flwr-datasets
 
 Main features
 -------------
diff --git a/datasets/doc/source/ref-api-flwr-datasets.rst b/datasets/doc/source/ref-api-flwr-datasets.rst
deleted file mode 100644
index 2e6a9e731add..000000000000
--- a/datasets/doc/source/ref-api-flwr-datasets.rst
+++ /dev/null
@@ -1,27 +0,0 @@
-flwr\_datasets (Python API reference)
-======================
-
-Federated Dataset
------------------
-.. autoclass:: flwr_datasets.federated_dataset.FederatedDataset
-   :members:
-
-
-partitioner
------------
-
-.. automodule:: flwr_datasets.partitioner
-
-
-Partitioner
------------
-
-.. autoclass:: flwr_datasets.partitioner.Partitioner
-   :members:
-
-
-IID Partitioner
----------------
-
-.. autoclass:: flwr_datasets.partitioner.IidPartitioner
-   :members:
diff --git a/dev/build-docs.sh b/dev/build-docs.sh
index 0c913c6fc1d8..45a4dfca0adf 100755
--- a/dev/build-docs.sh
+++ b/dev/build-docs.sh
@@ -13,8 +13,7 @@ cd examples/doc
 make docs
 
 cd $ROOT
-cd datasets/doc
-make docs
+./datasets/dev/build-flwr-datasets-docs.sh
 
 cd $ROOT
 cd doc

From 961367049b849a3635685eb566bac5461531cd98 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 23 Nov 2023 11:37:07 +0100
Subject: [PATCH 39/64] Update the transforms section for DataLoader (#2628)

---
 datasets/doc/source/tutorial-quickstart.rst | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/datasets/doc/source/tutorial-quickstart.rst b/datasets/doc/source/tutorial-quickstart.rst
index 8a70ee8854be..b4578fae0de9 100644
--- a/datasets/doc/source/tutorial-quickstart.rst
+++ b/datasets/doc/source/tutorial-quickstart.rst
@@ -51,20 +51,22 @@ For more detailed instructions, go to :doc:`how-to-use-with-pytorch`.
 
 PyTorch DataLoader
 ^^^^^^^^^^^^^^^^^^
-Transform the Dataset directly into the DataLoader::
+Transform the Dataset into the DataLoader, use the PyTorch transforms (`Compose` and all the others are also
+possible)::
 
   from torch.utils.data import DataLoader
   from torchvision.transforms import ToTensor
 
   transforms = ToTensor()
-  partition_torch = partition.map(
-        lambda img: {"img": transforms(img)}, input_columns="img"
-    ).with_format("torch")
+  def apply_transforms(batch):
+    batch["img"] = [transforms(img) for img in batch["img"]]
+    return batch
+  partition_torch = partition.with_transform(apply_transforms)
   dataloader = DataLoader(partition_torch, batch_size=64)
 
 NumPy
 ^^^^^
-NumPy can be used as input to the TensorFlow model and is very straightforward::
+NumPy can be used as input to the TensorFlow and scikit-learn models and it is very straightforward::
 
    partition_np = partition.with_format("numpy")
    X_train, y_train = partition_np["img"], partition_np["label"]

From 3e07f97dd37af92bb9c0b7714720a27cb0393eca Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 23 Nov 2023 12:38:52 +0100
Subject: [PATCH 40/64] Update the dataset creation docs (#2629)

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 datasets/doc/source/how-to-use-with-pytorch.rst | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/datasets/doc/source/how-to-use-with-pytorch.rst b/datasets/doc/source/how-to-use-with-pytorch.rst
index 497266dd1e69..85e7833b0869 100644
--- a/datasets/doc/source/how-to-use-with-pytorch.rst
+++ b/datasets/doc/source/how-to-use-with-pytorch.rst
@@ -10,7 +10,7 @@ Standard setup - download the dataset, choose the partitioning::
   partition = fds.load_partition(0, "train")
   centralized_dataset = fds.load_full("test")
 
-Determine the names of our features (you can alternatively do that directly on the Hugging Face website). The name can
+Determine the names of the features (you can alternatively do that directly on the Hugging Face website). The name can
 vary e.g. "img" or "image", "label" or "labels"::
 
   partition.features
@@ -38,7 +38,7 @@ That is why we iterate over all the samples from this batch and apply our transf
     return batch
 
   partition_torch = partition.with_transform(apply_transforms)
-  # At this point, you can check if you didn't make any mistakes by calling partition_torch[0]
+  # Now, you can check if you didn't make any mistakes by calling partition_torch[0]
   dataloader = DataLoader(partition_torch, batch_size=64)
 
 
@@ -70,8 +70,10 @@ If you want to divide the dataset, you can use (at any point before passing the
 Or you can simply calculate the indices yourself::
 
   partition_len = len(partition)
-  partition_train = partition[:int(0.8 * partition_len)]
-  partition_test = partition[int(0.8 * partition_len):]
+  # Split `partition` 80:20
+  num_train_examples = int(0.8 * partition_len)
+  partition_train = partition.select(range(num_train_examples)) ) # use first 80% 
+  partition_test = partition.select(range(num_train_examples, partition_len)) ) # use last 20%
 
 And during the training loop, you need to apply one change. With a typical dataloader, you get a list returned for each iteration::
 

From 6eca62b421516cc6eee7f6dd8a1081367d437cc9 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 23 Nov 2023 13:26:46 +0100
Subject: [PATCH 41/64] Add information how to see the features of the dataset
 (#2627)

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 datasets/doc/source/how-to-use-with-numpy.rst | 18 ++++++-
 .../doc/source/how-to-use-with-tensorflow.rst | 48 ++++++++++---------
 datasets/doc/source/tutorial-quickstart.rst   | 33 ++++++++++---
 3 files changed, 68 insertions(+), 31 deletions(-)

diff --git a/datasets/doc/source/how-to-use-with-numpy.rst b/datasets/doc/source/how-to-use-with-numpy.rst
index c3fbf85969e3..db80b712a13e 100644
--- a/datasets/doc/source/how-to-use-with-numpy.rst
+++ b/datasets/doc/source/how-to-use-with-numpy.rst
@@ -3,7 +3,7 @@ Use with NumPy
 
 Let's integrate ``flwr-datasets`` with NumPy.
 
-Prepare the desired partitioning::
+Create a ``FederatedDataset``::
 
   from flwr_datasets import FederatedDataset
 
@@ -11,6 +11,22 @@ Prepare the desired partitioning::
   partition = fds.load_partition(0, "train")
   centralized_dataset = fds.load_full("test")
 
+Inspect the names of the features::
+
+  partition.features
+
+In case of CIFAR10, you should see the following output.
+
+.. code-block:: none
+
+  {'img': Image(decode=True, id=None),
+  'label': ClassLabel(names=['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog',
+  'frog', 'horse', 'ship', 'truck'], id=None)}
+
+We will use the keys in the partition features in order to apply transformations to the data or pass it to a ML model.  Let's move to the transformations.
+
+NumPy
+-----
 Transform to NumPy::
 
   partition_np = partition.with_format("numpy")
diff --git a/datasets/doc/source/how-to-use-with-tensorflow.rst b/datasets/doc/source/how-to-use-with-tensorflow.rst
index 86a1f4e0da8a..a63145d9dffa 100644
--- a/datasets/doc/source/how-to-use-with-tensorflow.rst
+++ b/datasets/doc/source/how-to-use-with-tensorflow.rst
@@ -1,10 +1,32 @@
 Use with TensorFlow
 ===================
 
-Let's integrate ``flwr-datasets`` with TensorFlow. We show you three ways how to convert the data into the formats
+Let's integrate ``flwr-datasets`` with ``TensorFlow``. We show you three ways how to convert the data into the formats
 that ``TensorFlow``'s models expect.  Please note that, especially for the smaller datasets, the performance of the
 following methods is very close. We recommend you choose the method you are the most comfortable with.
 
+Create a ``FederatedDataset``::
+
+  from flwr_datasets import FederatedDataset
+
+  fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+  partition = fds.load_partition(0, "train")
+  centralized_dataset = fds.load_full("test")
+
+Inspect the names of the features::
+
+  partition.features
+
+In case of CIFAR10, you should see the following output.
+
+.. code-block:: none
+
+  {'img': Image(decode=True, id=None),
+  'label': ClassLabel(names=['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog',
+  'frog', 'horse', 'ship', 'truck'], id=None)}
+
+We will use the keys in the partition features in order to construct a `tf.data.Dataset <https://www.tensorflow.org/api_docs/python/tf/data/Dataset>_`. Let's move to the transformations.
+
 NumPy
 -----
 The first way is to transform the data into the NumPy arrays. It's an easier option that is commonly used. Feel free to
@@ -14,17 +36,7 @@ follow the :doc:`how-to-use-with-numpy` tutorial, especially if you are a beginn
 
 TensorFlow Dataset
 ------------------
-Work with ``TensorFlow Dataset`` abstraction.
-
-Standard setup::
-
-  from flwr_datasets import FederatedDataset
-
-  fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
-  partition = fds.load_partition(0, "train")
-  centralized_dataset = fds.load_full("test")
-
-Transformation to the TensorFlow Dataset::
+Transform the data to ``TensorFlow Dataset``::
 
   tf_dataset = partition.to_tf_dataset(columns="img", label_cols="label", batch_size=64,
                                      shuffle=True)
@@ -33,17 +45,7 @@ Transformation to the TensorFlow Dataset::
 
 TensorFlow Tensors
 ------------------
-Change the data type to TensorFlow Tensors (it's not the TensorFlow dataset).
-
-Standard setup::
-
-  from flwr_datasets import FederatedDataset
-
-  fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
-  partition = fds.load_partition(0, "train")
-  centralized_dataset = fds.load_full("test")
-
-Transformation to the TensorFlow Tensors ::
+Transform the data to the TensorFlow `tf.Tensor <https://www.tensorflow.org/api_docs/python/tf/Tensor>`_ (it's not the TensorFlow dataset)::
 
   data_tf = partition.with_format("tf")
   # Assuming you have defined your model and compiled it
diff --git a/datasets/doc/source/tutorial-quickstart.rst b/datasets/doc/source/tutorial-quickstart.rst
index b4578fae0de9..b93a08f234f2 100644
--- a/datasets/doc/source/tutorial-quickstart.rst
+++ b/datasets/doc/source/tutorial-quickstart.rst
@@ -5,11 +5,11 @@ Run Flower Datasets as fast as possible by learning only the essentials.
 
 Install Federated Datasets
 --------------------------
-Run on the command line
+On the command line, run
 
 .. code-block:: bash
 
-  python -m pip install flwr-datasets[vision]
+  python -m pip install "flwr-datasets[vision]"
 
 Install the ML framework
 ------------------------
@@ -28,12 +28,11 @@ PyTorch
 Choose the dataset
 ------------------
 Choose the dataset by going to Hugging Face `Datasets Hub <https://huggingface.co/datasets>`_ and searching for your
-dataset by name. Note that the name is case sensitive, so make sure to pass the correct name as the `dataset` parameter
-to `FederatedDataset`.
+dataset by name that you will pass to the `dataset` parameter of `FederatedDataset`. Note that the name is case sensitive.
 
 Partition the dataset
 ---------------------
-::
+To iid partition your dataset, choose the split you want to partition and the number of partitions::
 
   from flwr_datasets import FederatedDataset
 
@@ -42,12 +41,32 @@ Partition the dataset
   centralized_dataset = fds.load_full("test")
 
 Now you're ready to go. You have ten partitions created from the train split of the MNIST dataset and the test split
-for the centralized evaluation. We will convert the type of the dataset from Hugging Face's Dataset type to the one
+for the centralized evaluation. We will convert the type of the dataset from Hugging Face's `Dataset` type to the one
 supported by your framework.
 
+Display the features
+--------------------
+Determine the names of the features of your dataset (you can alternatively do that directly on the Hugging Face
+website). The names can vary along different datasets e.g. "img" or "image", "label" or "labels". You will also see
+the names of label categories. Type::
+
+  partition.features
+
+In case of CIFAR10, you should see the following output.
+
+.. code-block:: none
+
+  {'img': Image(decode=True, id=None),
+  'label': ClassLabel(names=['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog',
+  'frog', 'horse', 'ship', 'truck'], id=None)}
+
+Note that the image is denoted by "img" which is crucial for the next steps (conversion you the ML
+framework of your choice).
+
 Conversion
 ----------
-For more detailed instructions, go to :doc:`how-to-use-with-pytorch`.
+For more detailed instructions, go to :doc:`how-to-use-with-pytorch`, :doc:`how-to-use-with-numpy`, or
+:doc:`how-to-use-with-tensorflow`.
 
 PyTorch DataLoader
 ^^^^^^^^^^^^^^^^^^

From ee35b91d19dd3abf98f8cd08279ae33983156792 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 23 Nov 2023 15:35:51 +0100
Subject: [PATCH 42/64] Migrate PyTorch quickstart to Flower Datasets (#2314)

Co-authored-by: jafermarq <javier@flower.dev>
---
 examples/quickstart-pytorch/README.md        | 11 +++--
 examples/quickstart-pytorch/client.py        | 48 +++++++++++++++-----
 examples/quickstart-pytorch/pyproject.toml   |  5 +-
 examples/quickstart-pytorch/requirements.txt |  5 +-
 examples/quickstart-pytorch/run.sh           |  7 +--
 5 files changed, 51 insertions(+), 25 deletions(-)

diff --git a/examples/quickstart-pytorch/README.md b/examples/quickstart-pytorch/README.md
index f748894f4971..6de0dcf7ab32 100644
--- a/examples/quickstart-pytorch/README.md
+++ b/examples/quickstart-pytorch/README.md
@@ -1,7 +1,6 @@
 # Flower Example using PyTorch
 
-This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case.
-Running this example in itself is quite easy.
+This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
 
 ## Project Setup
 
@@ -56,18 +55,20 @@ Afterwards you are ready to start the Flower server as well as the clients. You
 python3 server.py
 ```
 
-Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminal windows and run the following commands.
+Now you are ready to start the Flower clients which will participate in the learning. We need to specify the node id to
+use different partitions of the data on different nodes.  To do so simply open two more terminal windows and run the
+following commands.
 
 Start client 1 in the first terminal:
 
 ```shell
-python3 client.py
+python3 client.py --node-id 0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-python3 client.py
+python3 client.py --node-id 1
 ```
 
 You will see that PyTorch is starting a federated training. Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) for a detailed explanation.
diff --git a/examples/quickstart-pytorch/client.py b/examples/quickstart-pytorch/client.py
index 6db7c8a855a0..8ce19a45403d 100644
--- a/examples/quickstart-pytorch/client.py
+++ b/examples/quickstart-pytorch/client.py
@@ -1,12 +1,13 @@
+import argparse
 import warnings
 from collections import OrderedDict
 
 import flwr as fl
+from flwr_datasets import FederatedDataset
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.utils.data import DataLoader
-from torchvision.datasets import CIFAR10
 from torchvision.transforms import Compose, Normalize, ToTensor
 from tqdm import tqdm
 
@@ -45,7 +46,9 @@ def train(net, trainloader, epochs):
     criterion = torch.nn.CrossEntropyLoss()
     optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
     for _ in range(epochs):
-        for images, labels in tqdm(trainloader):
+        for batch in tqdm(trainloader, "Training"):
+            images = batch["img"]
+            labels = batch["label"]
             optimizer.zero_grad()
             criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
             optimizer.step()
@@ -56,30 +59,53 @@ def test(net, testloader):
     criterion = torch.nn.CrossEntropyLoss()
     correct, loss = 0, 0.0
     with torch.no_grad():
-        for images, labels in tqdm(testloader):
+        for batch in tqdm(testloader, "Testing"):
+            images = batch["img"]
+            labels = batch["label"]
             outputs = net(images.to(DEVICE))
-            labels = labels.to(DEVICE)
             loss += criterion(outputs, labels).item()
             correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
     accuracy = correct / len(testloader.dataset)
     return loss, accuracy
 
 
-def load_data():
-    """Load CIFAR-10 (training and test set)."""
-    trf = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-    trainset = CIFAR10("./data", train=True, download=True, transform=trf)
-    testset = CIFAR10("./data", train=False, download=True, transform=trf)
-    return DataLoader(trainset, batch_size=32, shuffle=True), DataLoader(testset)
+def load_data(node_id):
+    """Load partition CIFAR10 data."""
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 3})
+    partition = fds.load_partition(node_id)
+    # Divide data on each node: 80% train, 20% test
+    partition_train_test = partition.train_test_split(test_size=0.2)
+    pytorch_transforms = Compose(
+        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+    )
+
+    def apply_transforms(batch):
+        """Apply transforms to the partition from FederatedDataset."""
+        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
+        return batch
+
+    partition_train_test = partition_train_test.with_transform(apply_transforms)
+    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
+    testloader = DataLoader(partition_train_test["test"], batch_size=32)
+    return trainloader, testloader
 
 
 # #############################################################################
 # 2. Federation of the pipeline with Flower
 # #############################################################################
 
+# Get node id
+parser = argparse.ArgumentParser(description="Flower")
+parser.add_argument(
+    "--node-id",
+    choices=[0, 1, 2],
+    type=int,
+    help="Partition of the dataset divided into 3 iid partitions created artificially.")
+node_id = parser.parse_args().node_id
+
 # Load model and data (simple CNN, CIFAR-10)
 net = Net().to(DEVICE)
-trainloader, testloader = load_data()
+trainloader, testloader = load_data(node_id=node_id)
 
 
 # Define Flower client
diff --git a/examples/quickstart-pytorch/pyproject.toml b/examples/quickstart-pytorch/pyproject.toml
index affdfee26d47..ec6a3af8c5b4 100644
--- a/examples/quickstart-pytorch/pyproject.toml
+++ b/examples/quickstart-pytorch/pyproject.toml
@@ -11,6 +11,7 @@ authors = ["The Flower Authors <hello@flower.dev>"]
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
-torch = "1.13.1"
-torchvision = "0.14.1"
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
+torch = "2.1.1"
+torchvision = "0.16.1"
 tqdm = "4.65.0"
diff --git a/examples/quickstart-pytorch/requirements.txt b/examples/quickstart-pytorch/requirements.txt
index 797ca6db6244..4e321e2cd0c2 100644
--- a/examples/quickstart-pytorch/requirements.txt
+++ b/examples/quickstart-pytorch/requirements.txt
@@ -1,4 +1,5 @@
 flwr>=1.0, <2.0
-torch==1.13.1
-torchvision==0.14.1
+flwr-datasets[vision]>=0.0.2, <1.0.0
+torch==2.1.1
+torchvision==0.16.1
 tqdm==4.65.0
diff --git a/examples/quickstart-pytorch/run.sh b/examples/quickstart-pytorch/run.sh
index d2bf34f834b1..cdace99bb8df 100755
--- a/examples/quickstart-pytorch/run.sh
+++ b/examples/quickstart-pytorch/run.sh
@@ -2,16 +2,13 @@
 set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
 
-# Download the CIFAR-10 dataset
-python -c "from torchvision.datasets import CIFAR10; CIFAR10('./data', download=True)"
-
 echo "Starting server"
 python server.py &
 sleep 3  # Sleep for 3s to give the server enough time to start
 
-for i in `seq 0 1`; do
+for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py &
+    python client.py --node-id "$i" &
 done
 
 # Enable CTRL+C to stop all background processes

From bd8c0e01d63578df9c079b0e5235f73b3fb6d97f Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Thu, 23 Nov 2023 19:03:50 +0100
Subject: [PATCH 43/64] Add files for chinese translation (#2631)

---
 .../zh_Hans/LC_MESSAGES/framework-docs.po     | 13608 ++++++++++++++++
 doc/locales/zh_Hans/LC_MESSAGES/sphinx.po     |    37 +
 2 files changed, 13645 insertions(+)
 create mode 100644 doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
 create mode 100644 doc/locales/zh_Hans/LC_MESSAGES/sphinx.po

diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
new file mode 100644
index 000000000000..f077264b4aab
--- /dev/null
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -0,0 +1,13608 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022 Flower Labs GmbH
+# This file is distributed under the same license as the Flower package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: Flower main\n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2023-11-23 18:31+0100\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language: zh_Hans\n"
+"Language-Team: zh_Hans <LL@li.org>\n"
+"Plural-Forms: nplurals=1; plural=0;\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.13.1\n"
+
+#: ../../source/contributor-explanation-architecture.rst:2
+msgid "Flower Architecture"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:5
+msgid "Edge Client Engine"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:7
+msgid ""
+"`Flower <https://flower.dev>`_ core framework architecture with Edge "
+"Client Engine"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:13
+msgid "Virtual Client Engine"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:15
+msgid ""
+"`Flower <https://flower.dev>`_ core framework architecture with Virtual "
+"Client Engine"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:21
+msgid "Virtual Client Engine and Edge Client Engine in the same workload"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:23
+msgid ""
+"`Flower <https://flower.dev>`_ core framework architecture with both "
+"Virtual Client Engine and Edge Client Engine"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:2
+msgid "Creating New Messages"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:4
+msgid ""
+"This is a simple guide for creating a new type of message between the "
+"server and clients in Flower."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:6
+msgid ""
+"Let's suppose we have the following example functions in "
+":code:`server.py` and :code:`numpy_client.py`..."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:8
+msgid "Server's side:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:17
+msgid "Client's side:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:26
+msgid ""
+"Let's now see what we need to implement in order to get this simple "
+"function between the server and client to work!"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:30
+msgid "Message Types for Protocol Buffers"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:32
+msgid ""
+"The first thing we need to do is to define a message type for the RPC "
+"system in :code:`transport.proto`. Note that we have to do it for both "
+"the request and response messages. For more details on the syntax of "
+"proto3, please see the  `official documentation "
+"<https://developers.google.com/protocol-buffers/docs/proto3>`_."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:35
+msgid "Within the :code:`ServerMessage` block:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:52
+msgid "Within the ClientMessage block:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:70
+msgid ""
+"Make sure to also add a field of the newly created message type in "
+":code:`oneof msg`."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:72
+msgid "Once that is done, we will compile the file with:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:78
+msgid "If it compiles succesfully, you should see the following message:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:87
+msgid "Serialization and Deserialization Functions"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:89
+msgid ""
+"Our next step is to add functions to serialize and deserialize Python "
+"datatypes to or from our defined RPC message types. You should add these "
+"functions in :code:`serde.py`."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:91
+msgid "The four functions:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:112
+msgid "Sending the Message from the Server"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:114
+msgid ""
+"Now write the request function in your Client Proxy class (e.g., "
+":code:`grpc_client_proxy.py`) using the serde functions you just created:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:128
+msgid "Receiving the Message by the Client"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:130
+msgid ""
+"Last step! Modify the code in :code:`message_handler.py` to check the "
+"field of your message and call the :code:`example_response` function. "
+"Remember to use the serde functions!"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:132
+msgid "Within the handle function:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:139
+msgid "And add a new function:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:149
+msgid "Hopefully, when you run your program you will get the intended result!"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:2
+msgid "Contribute translations"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:4
+msgid ""
+"Since `Flower 1.5 <https://flower.dev/docs/framework/ref-"
+"changelog.html#v1-5-0-2023-08-31>`_ we have introduced translations to "
+"our doc pages, but, as you might have noticed, the translations are often"
+" imperfect. If you speak languages other than English, you might be able "
+"to help us in our effort to make Federated Learning accessible to as many"
+" people as possible by contributing to those translations! This might "
+"also be a great opportunity for those wanting to become open source "
+"contributors with little prerequistes."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:13
+msgid ""
+"Our translation project is publicly available over on `Weblate "
+"<https://hosted.weblate.org/projects/flower-docs/framework/>`_, this "
+"where most of the work will happen."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:18
+msgid "Contribute to existing languages"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:23
+msgid ""
+"The first thing you will need to do in order to contribute is to create a"
+" free Weblate account on this `page "
+"<https://hosted.weblate.org/accounts/register/>`_. More information about"
+" profile settings can be found `here "
+"<https://docs.weblate.org/en/latest/user/profile.html>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:29
+msgid ""
+"Once you are signed in to Weblate, you can navigate to the `Flower "
+"Framework project <https://hosted.weblate.org/projects/flower-"
+"docs/framework/>`_. Here, you should see the different existing languages"
+" that can be found on the website."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:34
+msgid ""
+"Once you have selected the language you want to contribute to, you should"
+" see a similar interface to this:"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:39
+msgid ""
+"The most straight forward option here is to click on the ``Translate`` "
+"button on the top right (in the ``Translation status`` section). This "
+"will automatically bring you to the translation interface for "
+"untranslated strings."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:43
+msgid "This is what the interface looks like:"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:47
+msgid ""
+"You input your translation in the textbox at the top and then, once you "
+"are happy with it, you either press ``Save and continue`` (to save the "
+"translation and go to the next untranslated string), ``Save and stay`` "
+"(to save the translation and stay on the same page), ``Suggest`` (to add "
+"your translation to suggestions for other users to view), or ``Skip`` (to"
+" go to the next untranslated string without saving anything)."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:54
+msgid ""
+"In order to help with the translations, you can see on the bottom the "
+"``Nearby strings``, the ``Comments`` (from other contributors), the "
+"``Automatic suggestions`` (from machine translation engines), the "
+"translations in ``Other languages``, and the ``History`` of translations "
+"for this string."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:59
+msgid ""
+"On the right, under the ``String information`` section, you can also "
+"click the link under ``Source string location`` in order to view the "
+"source of the doc file containing the string."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:63
+msgid ""
+"For more information about translating using Weblate, you can check out "
+"this `in-depth guide "
+"<https://docs.weblate.org/en/latest/user/translating.html>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:67
+msgid "Add new languages"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:69
+msgid ""
+"If you want to add a new language, you will first have to contact us, "
+"either on `Slack <https://flower.dev/join-slack>`_, or by opening an "
+"issue on our `GitHub repo <https://github.com/adap/flower>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:2
+msgid "Develop in VSCode Dev Containers"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:4
+msgid ""
+"When working on the Flower framework we want to ensure that all "
+"contributors use the same developer environment to format code or run "
+"tests. For this purpose we are using the VSCode Remote Containers "
+"extension. What is it? Read the following quote:"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:7
+msgid ""
+"The Visual Studio Code Remote - Containers extension lets you use a "
+"Docker container as a fully-featured development environment. It allows "
+"you to open any folder inside (or mounted into) a container and take "
+"advantage of Visual Studio Code's full feature set. A "
+":code:`devcontainer.json` file in your project tells VS Code how to "
+"access (or create) a development container with a well-defined tool and "
+"runtime stack. This container can be used to run an application or to "
+"separate tools, libraries, or runtimes needed for working with a "
+"codebase."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:9
+msgid ""
+"Workspace files are mounted from the local file system or copied or "
+"cloned into the container. Extensions are installed and run inside the "
+"container, where they have full access to the tools, platform, and file "
+"system. This means that you can seamlessly switch your entire development"
+" environment just by connecting to a different container."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:11
+msgid ""
+"Source: `Official VSCode documentation "
+"<https://code.visualstudio.com/docs/remote/containers>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:15
+msgid "Getting started"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:17
+msgid ""
+"Configuring and setting up the :code:`Dockerfile` as well the "
+"configuration for the devcontainer can be a bit more involved. The good "
+"thing is you want have to do it. Usually it should be enough to install "
+"Docker on your system and ensure its available on your command line. "
+"Additionally, install the `VSCode Containers Extension <vscode:extension"
+"/ms-vscode-remote.remote-containers>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:19
+msgid ""
+"Now you should be good to go. When starting VSCode, it will ask you to "
+"run in the container environment and - if you confirm - automatically "
+"build the container and use it. To manually instruct VSCode to use the "
+"devcontainer, you can, after installing the extension, click the green "
+"area in the bottom left corner of your VSCode window and select the "
+"option *(Re)Open Folder in Container*."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:21
+msgid ""
+"In some cases your setup might be more involved. For those cases consult "
+"the following sources:"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:23
+msgid ""
+"`Developing inside a Container "
+"<https://code.visualstudio.com/docs/remote/containers#_system-"
+"requirements>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:24
+msgid ""
+"`Remote development in Containers "
+"<https://code.visualstudio.com/docs/remote/containers-tutorial>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:2
+msgid "Install development versions"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:5
+msgid "Install development versions of Flower"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:8
+msgid "Using Poetry (recommended)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:10
+msgid ""
+"Install a ``flwr`` pre-release from PyPI: update the ``flwr`` dependency "
+"in ``pyproject.toml`` and then reinstall (don't forget to delete "
+"``poetry.lock`` (``rm poetry.lock``) before running ``poetry install``)."
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:12
+msgid ""
+"``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (without "
+"extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:13
+msgid ""
+"``flwr = { version = \"1.0.0a0\", allow-prereleases = true, extras = "
+"[\"simulation\"] }`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:15
+msgid ""
+"Install ``flwr`` from a local copy of the Flower source code via "
+"``pyproject.toml``:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:17
+msgid "``flwr = { path = \"../../\", develop = true }`` (without extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:18
+msgid ""
+"``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] "
+"}`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:20
+msgid "Install ``flwr`` from a local wheel file via ``pyproject.toml``:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:22
+msgid ""
+"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\" }`` (without"
+" extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:23
+msgid ""
+"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\", extras = "
+"[\"simulation\"] }`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:25
+msgid ""
+"Please refer to the Poetry documentation for further details: `Poetry "
+"Dependency Specification <https://python-poetry.org/docs/dependency-"
+"specification/>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:28
+msgid "Using pip (recommended on Colab)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:30
+msgid "Install a ``flwr`` pre-release from PyPI:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:32
+msgid "``pip install -U --pre flwr`` (without extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:33
+msgid "``pip install -U --pre flwr[simulation]`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:35
+msgid ""
+"Python packages can be installed from git repositories. Use one of the "
+"following commands to install the Flower directly from GitHub."
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:37
+msgid "Install ``flwr`` from the default GitHub branch (``main``):"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:39
+msgid ""
+"``pip install flwr@git+https://github.com/adap/flower.git`` (without "
+"extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:40
+msgid ""
+"``pip install flwr[simulation]@git+https://github.com/adap/flower.git`` "
+"(with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:42
+msgid "Install ``flwr`` from a specific GitHub branch (``branch-name``):"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:44
+msgid ""
+"``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` "
+"(without extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:45
+msgid ""
+"``pip install flwr[simulation]@git+https://github.com/adap/flower.git"
+"@branch-name`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:49
+msgid "Open Jupyter Notebooks on Google Colab"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:51
+msgid ""
+"Open the notebook ``doc/source/tutorial-get-started-with-flower-"
+"pytorch.ipynb``:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:53
+msgid ""
+"https://colab.research.google.com/github/adap/flower/blob/main/doc/source"
+"/tutorial-get-started-with-flower-pytorch.ipynb"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:55
+msgid ""
+"Open a development version of the same notebook from branch `branch-name`"
+" by changing ``main`` to ``branch-name`` (right after ``blob``):"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:57
+msgid ""
+"https://colab.research.google.com/github/adap/flower/blob/branch-"
+"name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:59
+msgid "Install a `whl` on Google Colab:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:61
+msgid ""
+"In the vertical icon grid on the left hand side, select ``Files`` > "
+"``Upload to session storage``"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:62
+msgid "Upload the whl (e.g., ``flwr-1.6.0-py3-none-any.whl``)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:63
+msgid ""
+"Change ``!pip install -q 'flwr[simulation]' torch torchvision "
+"matplotlib`` to ``!pip install -q 'flwr-1.6.0-py3-none-"
+"any.whl[simulation]' torch torchvision matplotlib``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:2
+msgid "Release Flower"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:4
+msgid ""
+"This document describes the current release process. It may or may not "
+"change in the future."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:7
+msgid "Before the release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:9
+msgid ""
+"Update the changelog (``changelog.md``) with all relevant changes that "
+"happened after the last release. If the last release was tagged "
+"``v1.2.0``, you can use the following URL to see all commits that got "
+"merged into ``main`` since then:"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:11
+msgid ""
+"`GitHub: Compare v1.2.0...main "
+"<https://github.com/adap/flower/compare/v1.2.0...main>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:13
+msgid ""
+"Thank the authors who contributed since the last release. This can be "
+"done by running the ``./dev/add-shortlog.sh`` convenience script (it can "
+"be ran multiple times and will update the names in the list if new "
+"contributors were added in the meantime)."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:16
+msgid "During the release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:18
+msgid ""
+"The version number of a release is stated in ``pyproject.toml``. To "
+"release a new version of Flower, the following things need to happen (in "
+"that order):"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:20
+msgid ""
+"Update the ``changelog.md`` section header ``Unreleased`` to contain the "
+"version number and date for the release you are building. Create a pull "
+"request with the change."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:21
+msgid ""
+"Tag the release commit with the version number as soon as the PR is "
+"merged: ``git tag v0.12.3``, then ``git push --tags``. This will create a"
+" draft release on GitHub containing the correct artifacts and the "
+"relevant part of the changelog."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:22
+msgid "Check the draft release on GitHub, and if everything is good, publish it."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:25
+msgid "After the release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:27
+msgid "Create a pull request which contains the following changes:"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:29
+msgid "Increase the minor version in ``pyproject.toml`` by one."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:30
+msgid "Update all files which contain the current version number if necessary."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:31
+msgid "Add a new ``Unreleased`` section in ``changelog.md``."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:33
+msgid ""
+"Merge the pull request on the same day (i.e., before a new nighly release"
+" gets published to PyPI)."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:36
+msgid "Publishing a pre-release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:39
+msgid "Pre-release naming"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:41
+msgid ""
+"PyPI supports pre-releases (alpha, beta, release candiate). Pre-releases "
+"MUST use one of the following naming patterns:"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:43
+msgid "Alpha: ``MAJOR.MINOR.PATCHaN``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:44
+msgid "Beta: ``MAJOR.MINOR.PATCHbN``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:45
+msgid "Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:47
+msgid "Examples include:"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:49
+msgid "``1.0.0a0``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:50
+msgid "``1.0.0b0``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:51
+msgid "``1.0.0rc0``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:52
+msgid "``1.0.0rc1``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:54
+msgid ""
+"This is in line with PEP-440 and the recommendations from the Python "
+"Packaging Authority (PyPA):"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:57
+msgid "`PEP-440 <https://peps.python.org/pep-0440/>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:58
+msgid ""
+"`PyPA Choosing a versioning scheme "
+"<https://packaging.python.org/en/latest/guides/distributing-packages-"
+"using-setuptools/#choosing-a-versioning-scheme>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:60
+msgid ""
+"Note that the approach defined by PyPA is not compatible with SemVer "
+"2.0.0 spec, for details consult the `Semantic Versioning Specification "
+"<https://semver.org/spec/v2.0.0.html#spec-item-11>`_ (specifically item "
+"11 on precedence)."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:63
+msgid "Pre-release classification"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:65
+msgid "Should the next pre-release be called alpha, beta, or release candidate?"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:67
+msgid ""
+"RC: feature complete, no known issues (apart from issues that are "
+"classified as \"won't fix\" for the next stable release) - if no issues "
+"surface this will become the next stable release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:68
+msgid "Beta: feature complete, allowed to have known issues"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:69
+msgid "Alpha: not feature complete, allowed to have known issues"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:2
+msgid "Set up a virtual env"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:4
+msgid ""
+"It is recommended to run your Python setup within a virtual environment. "
+"This guide shows three different examples how to create a virtual "
+"environment with pyenv virtualenv, poetry, or Anaconda. You can follow "
+"the instructions or choose your preferred setup."
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:9
+msgid "Python Version"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:11
+#: ../../source/how-to-install-flower.rst:8
+msgid ""
+"Flower requires at least `Python 3.8 <https://docs.python.org/3.8/>`_, "
+"but `Python 3.10 <https://docs.python.org/3.10/>`_ or above is "
+"recommended."
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14
+msgid "Virutualenv with Pyenv/Virtualenv"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:16
+msgid ""
+"One of the recommended virtual environment is `pyenv "
+"<https://github.com/pyenv/pyenv>`_/`virtualenv <https://github.com/pyenv"
+"/pyenv-virtualenv>`_. Please see `Flower examples "
+"<https://github.com/adap/flower/tree/main/examples/>`_ for details."
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:18
+msgid ""
+"Once Pyenv is set up, you can use it to install `Python Version 3.10 "
+"<https://docs.python.org/3.10/>`_ or above:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:24
+msgid "Create the virtualenv with:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:31
+msgid "Activate the virtualenv by running the following command:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:39
+msgid "Virtualenv with Poetry"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:41
+msgid ""
+"The Flower examples are based on `Poetry <https://python-"
+"poetry.org/docs/>`_ to manage dependencies. After installing Poetry you "
+"simply create a virtual environment with:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:47
+msgid ""
+"If you open a new terminal you can activate the previously created "
+"virtual environment with the following command:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:55
+msgid "Virtualenv with Anaconda"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:57
+msgid ""
+"If you prefer to use Anaconda for your virtual environment then install "
+"and setup the `conda <https://docs.conda.io/projects/conda/en/latest"
+"/user-guide/install/index.html>`_  package. After setting it up you can "
+"create a virtual environment with:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:63
+msgid "and activate the virtual environment with:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:71
+msgid "And then?"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:73
+msgid ""
+"As soon as you created your virtual environment you clone one of the "
+"`Flower examples <https://github.com/adap/flower/tree/main/examples/>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:2
+msgid "Write documentation"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:6
+msgid "Project layout"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:8
+msgid ""
+"The Flower documentation lives in the ``doc`` directory. The Sphinx-based"
+" documentation system supports both reStructuredText (``.rst`` files) and"
+" Markdown (``.md`` files)."
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:10
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119
+msgid ""
+"Note that, in order to build the documentation locally (with ``poetry run"
+" make html``, like described below), `Pandoc "
+"<https://pandoc.org/installing.html>_` needs to be installed on the "
+"system."
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:14
+msgid "Edit an existing page"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:16
+msgid "Edit an existing ``.rst`` (or ``.md``) file under ``doc/source/``"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:17
+#: ../../source/contributor-how-to-write-documentation.rst:27
+msgid "Compile the docs: ``cd doc``, then ``poetry run make html``"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:18
+#: ../../source/contributor-how-to-write-documentation.rst:28
+msgid "Open ``doc/build/html/index.html`` in the browser to check the result"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:22
+msgid "Create a new page"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:24
+msgid "Add new ``.rst`` file under ``doc/source/``"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:25
+msgid "Add content to the new ``.rst`` file"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:26
+msgid "Link to the new rst from ``index.rst``"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:2
+msgid "Good first contributions"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:4
+msgid ""
+"We welcome contributions to Flower! However, it is not always easy to "
+"know where to start. We therefore put together a few recommendations on "
+"where to start to increase your chances of getting your PR accepted into "
+"the Flower codebase."
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:11
+msgid "Where to start"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:13
+msgid ""
+"Until the Flower core library matures it will be easier to get PR's "
+"accepted if they only touch non-core areas of the codebase. Good "
+"candidates to get started are:"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:17
+msgid "Documentation: What's missing? What could be expressed more clearly?"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:18
+msgid "Baselines: See below."
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:19
+msgid "Examples: See below."
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:23
+msgid "Request for Flower Baselines"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:25
+msgid ""
+"If you are not familiar with Flower Baselines, you should probably check-"
+"out our `contributing guide for baselines <https://flower.dev/docs"
+"/contributing-baselines.html>`_."
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:27
+msgid ""
+"You should then check out the open `issues "
+"<https://github.com/adap/flower/issues?q=is%3Aopen+is%3Aissue+label%3A%22new+baseline%22>`_"
+" for baseline requests. If you find a baseline that you'd like to work on"
+" and that has no assignes, feel free to assign it to yourself and start "
+"working on it!"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:31
+msgid ""
+"Otherwise, if you don't find a baseline you'd like to work on, be sure to"
+" open a new issue with the baseline request template!"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:34
+msgid "Request for examples"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:36
+msgid ""
+"We wish we had more time to write usage examples because we believe they "
+"help users to get started with building what they want to build. Here are"
+" a few ideas where we'd be happy to accept a PR:"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:40
+msgid "Llama 2 fine-tuning, with Hugging Face Transformers and PyTorch"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:41
+msgid "XGBoost"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:42
+msgid "Android ONNX on-device training"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:2
+msgid "Secure Aggregation Protocols"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:4
+msgid ""
+"Include SecAgg, SecAgg+, and LightSecAgg protocol. The LightSecAgg "
+"protocol has not been implemented yet, so its diagram and abstraction may"
+" not be accurate in practice. The SecAgg protocol can be considered as a "
+"special case of the SecAgg+ protocol."
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:8
+msgid "The :code:`SecAgg+` abstraction"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:10
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:161
+msgid ""
+"In this implementation, each client will be assigned with a unique index "
+"(int) for secure aggregation, and thus many python dictionaries used have"
+" keys of int type rather than ClientProxy type."
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:65
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:198
+msgid ""
+"The Flower server will execute and process received results in the "
+"following order:"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:159
+msgid "The :code:`LightSecAgg` abstraction"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:271
+msgid "Types"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:2
+msgid "Contribute on GitHub"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:4
+msgid ""
+"This guide is for people who want to get involved with Flower, but who "
+"are not used to contributing to GitHub projects."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:6
+msgid ""
+"If you're familiar with how contributing on GitHub works, you can "
+"directly checkout our `getting started guide for contributors "
+"<https://flower.dev/docs/getting-started-for-contributors.html>`_ and "
+"examples of `good first contributions <https://flower.dev/docs/good-"
+"first-contributions.html>`_."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:12
+msgid "Setting up the repository"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:23
+msgid "**Create a GitHub account and setup Git**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:15
+msgid ""
+"Git is a distributed version control tool. This allows for an entire "
+"codebase's history to be stored and every developer's machine. It is a "
+"software that will need to be installed on your local machine, you can "
+"follow this `guide <https://docs.github.com/en/get-started/quickstart"
+"/set-up-git>`_ to set it up."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:18
+msgid ""
+"GitHub, itself, is a code hosting platform for version control and "
+"collaboration. It allows for everyone to collaborate and work from "
+"anywhere on remote repositories."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:20
+msgid ""
+"If you haven't already, you will need to create an account on `GitHub "
+"<https://github.com/signup>`_."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:22
+msgid ""
+"The idea behind the generic Git and GitHub workflow boils down to this: "
+"you download code from a remote repository on GitHub, make changes "
+"locally and keep track of them using Git and then you upload your new "
+"history back to GitHub."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:34
+msgid "**Forking the Flower repository**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:26
+msgid ""
+"A fork is a personal copy of a GitHub repository. To create one for "
+"Flower, you must navigate to https://github.com/adap/flower (while "
+"connected to your GitHub account) and click the ``Fork`` button situated "
+"on the top right of the page."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:31
+msgid ""
+"You can change the name if you want, but this is not necessary as this "
+"version of Flower will be yours and will sit inside your own account "
+"(i.e., in your own list of repositories). Once created, you should see on"
+" the top left corner that you are looking at your own version of Flower."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+msgid "**Cloning your forked repository**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:37
+msgid ""
+"The next step is to download the forked repository on your machine to be "
+"able to make changes to it. On your forked repository page, you should "
+"first click on the ``Code`` button on the right, this will give you the "
+"ability to copy the HTTPS link of the repository."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:43
+msgid ""
+"Once you copied the \\<URL\\>, you can open a terminal on your machine, "
+"navigate to the place you want to download the repository to and type:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+msgid ""
+"This will create a `flower/` (or the name of your fork if you renamed it)"
+" folder in the current working directory."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:68
+msgid "**Add origin**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:52
+msgid "You can then go into the repository folder:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:58
+msgid ""
+"And here we will need to add an origin to our repository. The origin is "
+"the \\<URL\\> of the remote fork repository. To obtain it, we can do as "
+"previously mentioned by going to our fork repository on our GitHub "
+"account and copying the link."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:63
+msgid ""
+"Once the \\<URL\\> is copied, we can type the following command in our "
+"terminal:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:92
+msgid "**Add upstream**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:71
+msgid ""
+"Now we will add an upstream address to our repository. Still in the same "
+"directroy, we must run the following command:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:78
+msgid "The following diagram visually explains what we did in the previous steps:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:82
+msgid ""
+"The upstream is the GitHub remote address of the parent repository (in "
+"this case Flower), i.e. the one we eventually want to contribute to and "
+"therefore need an up-to-date history of. The origin is just the GitHub "
+"remote address of the forked repository we created, i.e. the copy (fork) "
+"in our own account."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:86
+msgid ""
+"To make sure our local version of the fork is up-to-date with the latest "
+"changes from the Flower repository, we can execute the following command:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:95
+msgid "Setting up the coding environment"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:97
+msgid ""
+"This can be achieved by following this `getting started guide for "
+"contributors`_ (note that you won't need to clone the repository). Once "
+"you are able to write code and test it, you can finally start making "
+"changes!"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:102
+msgid "Making changes"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:104
+msgid ""
+"Before making any changes make sure you are up-to-date with your "
+"repository:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:110
+msgid "And with Flower's repository:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:124
+msgid "**Create a new branch**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:117
+msgid ""
+"To make the history cleaner and easier to work with, it is good practice "
+"to create a new branch for each feature/project that needs to be "
+"implemented."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:120
+msgid ""
+"To do so, just run the following command inside the repository's "
+"directory:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+msgid "**Make changes**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+msgid "Write great code and create wonderful changes using your favorite editor!"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:140
+msgid "**Test and format your code**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:130
+msgid ""
+"Don't forget to test and format your code! Otherwise your code won't be "
+"able to be merged into the Flower repository. This is done so the "
+"codebase stays consistent and easy to understand."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:133
+msgid "To do so, we have written a few scripts that you can execute:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:152
+msgid "**Stage changes**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:143
+msgid ""
+"Before creating a commit that will update your history, you must specify "
+"to Git which files it needs to take into account."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:145
+msgid "This can be done with:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:151
+msgid ""
+"To check which files have been modified compared to the last version "
+"(last commit) and to see which files are staged for commit, you can use "
+"the :code:`git status` command."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:162
+msgid "**Commit changes**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:155
+msgid ""
+"Once you have added all the files you wanted to commit using :code:`git "
+"add`, you can finally create your commit using this command:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:161
+msgid ""
+"The \\<commit_message\\> is there to explain to others what the commit "
+"does. It should be written in an imperative style and be concise. An "
+"example would be :code:`git commit -m \"Add images to README\"`."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:173
+msgid "**Push the changes to the fork**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:165
+msgid ""
+"Once we have committed our changes, we have effectively updated our local"
+" history, but GitHub has no way of knowing this unless we push our "
+"changes to our origin's remote address:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:172
+msgid ""
+"Once this is done, you will see on the GitHub that your forked repo was "
+"updated with the changes you have made."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:176
+msgid "Creating and merging a pull request (PR)"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:203
+msgid "**Create the PR**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:179
+msgid ""
+"Once you have pushed changes, on the GitHub webpage of your repository "
+"you should see the following message:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:183
+msgid "Otherwise you can always find this option in the `Branches` page."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:185
+msgid ""
+"Once you click the `Compare & pull request` button, you should see "
+"something similar to this:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:189
+msgid "At the top you have an explanation of which branch will be merged where:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:193
+msgid ""
+"In this example you can see that the request is to merge the branch "
+"``doc-fixes`` from my forked repository to branch ``main`` from the "
+"Flower repository."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:195
+msgid ""
+"The input box in the middle is there for you to describe what your PR "
+"does and to link it to existing issues. We have placed comments (that "
+"won't be rendered once the PR is opened) to guide you through the "
+"process."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:198
+msgid ""
+"At the bottom you will find the button to open the PR. This will notify "
+"reviewers that a new PR has been opened and that they should look over it"
+" to merge or to request changes."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:201
+msgid ""
+"If your PR is not yet ready for review, and you don't want to notify "
+"anyone, you have the option to create a draft pull request:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:206
+msgid "**Making new changes**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:206
+msgid ""
+"Once the PR has been opened (as draft or not), you can still push new "
+"commits to it the same way we did before, by making changes to the branch"
+" associated with the PR."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:228
+msgid "**Review the PR**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:209
+msgid ""
+"Once the PR has been opened or once the draft PR has been marked as "
+"ready, a review from code owners will be automatically requested:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:213
+msgid ""
+"Code owners will then look into the code, ask questions, request changes "
+"or validate the PR."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:215
+msgid "Merging will be blocked if there are ongoing requested changes."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:219
+msgid ""
+"To resolve them, just push the necessary changes to the branch associated"
+" with the PR:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:223
+msgid "And resolve the conversation:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:227
+msgid ""
+"Once all the conversations have been resolved, you can re-request a "
+"review."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:248
+msgid "**Once the PR is merged**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:231
+msgid ""
+"If all the automatic tests have passed and reviewers have no more changes"
+" to request, they can approve the PR and merge it."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:235
+msgid ""
+"Once it is merged, you can delete the branch on GitHub (a button should "
+"appear to do so) and also delete it locally by doing:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:242
+msgid "Then you should update your forked repository by doing:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:251
+msgid "Example of first contribution"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:254
+msgid "Problem"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:256
+msgid ""
+"For our documentation, we’ve started to use the `Diàtaxis framework "
+"<https://diataxis.fr/>`_."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:258
+msgid ""
+"Our “How to” guides should have titles that continue the sencence “How to"
+" …”, for example, “How to upgrade to Flower 1.0”."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:260
+msgid ""
+"Most of our guides do not follow this new format yet, and changing their "
+"title is (unfortunately) more involved than one might think."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:262
+msgid ""
+"This issue is about changing the title of a doc from present continious "
+"to present simple."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:264
+msgid ""
+"Let's take the example of “Saving Progress” which we changed to “Save "
+"Progress”. Does this pass our check?"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:266
+msgid "Before: ”How to saving progress” ❌"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:268
+msgid "After: ”How to save progress” ✅"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:271
+msgid "Solution"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:273
+msgid ""
+"This is a tiny change, but it’ll allow us to test your end-to-end setup. "
+"After cloning and setting up the Flower repo, here’s what you should do:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:275
+msgid "Find the source file in `doc/source`"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:276
+msgid ""
+"Make the change in the `.rst` file (beware, the dashes under the title "
+"should be the same length as the title itself)"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:277
+msgid ""
+"Build the docs and check the result: `<https://flower.dev/docs/writing-"
+"documentation.html#edit-an-existing-page>`_"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:280
+msgid "Rename file"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:282
+msgid ""
+"You might have noticed that the file name still reflects the old wording."
+" If we just change the file, then we break all existing links to it - it "
+"is **very important** to avoid that, breaking links can harm our search "
+"engine ranking."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:285
+msgid "Here’s how to change the file name:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:287
+msgid "Change the file name to `save-progress.rst`"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:288
+msgid "Add a redirect rule to `doc/source/conf.py`"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:290
+msgid ""
+"This will cause a redirect from `saving-progress.html` to `save-"
+"progress.html`, old links will continue to work."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:293
+msgid "Apply changes in the index file"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:295
+msgid ""
+"For the lateral navigation bar to work properly, it is very important to "
+"update the `index.rst` file as well. This is where we define the whole "
+"arborescence of the navbar."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:298
+msgid "Find and modify the file name in `index.rst`"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:301
+msgid "Open PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:303
+msgid ""
+"Commit the changes (commit messages are always imperative: “Do "
+"something”, in this case “Change …”)"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:304
+msgid "Push the changes to your fork"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:305
+msgid "Open a PR (as shown above)"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:306
+msgid "Wait for it to be approved!"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:307
+msgid "Congrats! 🥳 You're now officially a Flower contributor!"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:311
+msgid "How to write a good PR title"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:313
+msgid ""
+"A well-crafted PR title helps team members quickly understand the purpose"
+" and scope of the changes being proposed. Here's a guide to help you "
+"write a good GitHub PR title:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:315
+msgid ""
+"1. Be Clear and Concise: Provide a clear summary of the changes in a "
+"concise manner. 1. Use Actionable Verbs: Start with verbs like \"Add,\" "
+"\"Update,\" or \"Fix\" to indicate the purpose. 1. Include Relevant "
+"Information: Mention the affected feature or module for context. 1. Keep "
+"it Short: Avoid lengthy titles for easy readability. 1. Use Proper "
+"Capitalization and Punctuation: Follow grammar rules for clarity."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:321
+msgid ""
+"Let's start with a few examples for titles that should be avoided because"
+" they do not provide meaningful information:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:323
+msgid "Implement Algorithm"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:324
+msgid "Database"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:325
+msgid "Add my_new_file.py to codebase"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:326
+msgid "Improve code in module"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:327
+msgid "Change SomeModule"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:329
+msgid ""
+"Here are a few positive examples which provide helpful information "
+"without repeating how they do it, as that is already visible in the "
+"\"Files changed\" section of the PR:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:331
+msgid "Update docs banner to mention Flower Summit 2023"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:332
+msgid "Remove unnecessary XGBoost dependency"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:333
+msgid "Remove redundant attributes in strategies subclassing FedAvg"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:334
+msgid "Add CI job to deploy the staging system when the `main` branch changes"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:335
+msgid ""
+"Add new amazing library which will be used to improve the simulation "
+"engine"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:339
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:548
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:946
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:747
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:713
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:367
+msgid "Next steps"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:341
+msgid ""
+"Once you have made your first PR, and want to contribute more, be sure to"
+" check out the following :"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:343
+msgid ""
+"`Good first contributions <https://flower.dev/docs/framework/contributor-"
+"ref-good-first-contributions.html>`_, where you should particularly look "
+"into the :code:`baselines` contributions."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:2
+msgid "Get started as a contributor"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5
+msgid "Prerequisites"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:7
+msgid "`Python 3.7 <https://docs.python.org/3.7/>`_ or above"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:8
+msgid "`Poetry 1.3 <https://python-poetry.org/>`_ or above"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:9
+msgid "(Optional) `pyenv <https://github.com/pyenv/pyenv>`_"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:10
+msgid "(Optional) `pyenv-virtualenv <https://github.com/pyenv/pyenv-virtualenv>`_"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:12
+msgid ""
+"Flower uses :code:`pyproject.toml` to manage dependencies and configure "
+"development tools (the ones which support it). Poetry is a build tool "
+"which supports `PEP 517 <https://www.python.org/dev/peps/pep-0517/>`_."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:18
+msgid "Developer Machine Setup"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:20
+msgid ""
+"First, clone the `Flower repository <https://github.com/adap/flower>`_ "
+"from GitHub::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:26
+msgid ""
+"Second, create a virtual environment (and activate it). If you chose to "
+"use :code:`pyenv` (with the :code:`pyenv-virtualenv` plugin) and already "
+"have it installed , you can use the following convenience script (by "
+"default it will use :code:`Python 3.8.17`, but you can change it by "
+"providing a specific :code:`<version>`)::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:33
+msgid ""
+"If you don't have :code:`pyenv` installed, you can use the following "
+"script that will install pyenv, set it up and create the virtual "
+"environment (with :code:`Python 3.8.17` by default)::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:39
+msgid ""
+"Third, install the Flower package in development mode (think :code:`pip "
+"install -e`) along with all necessary dependencies::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46
+msgid "Convenience Scripts"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:48
+msgid ""
+"The Flower repository contains a number of convenience scripts to make "
+"recurring development tasks easier and less error-prone. See the "
+":code:`/dev` subdirectory for a full list. The following scripts are "
+"amonst the most important ones:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54
+msgid "Create/Delete Virtual Environment"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62
+msgid "Compile ProtoBuf Definitions"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69
+msgid "Auto-Format Code"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:76
+msgid "Run Linters and Tests"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:83
+msgid "Run Github Actions (CI) locally"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85
+msgid ""
+"Developers could run the full set of Github Actions workflows under their"
+" local environment by using `Act <https://github.com/nektos/act>_`. "
+"Please refer to the installation instructions under the linked repository"
+" and run the next command under Flower main cloned repository folder::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92
+msgid ""
+"The Flower default workflow would run by setting up the required Docker "
+"machines underneath."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:97
+msgid "Build Release"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99
+msgid ""
+"Flower uses Poetry to build releases. The necessary command is wrapped in"
+" a simple script::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:104
+msgid ""
+"The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in"
+" the :code:`/dist` subdirectory."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:109
+msgid "Build Documentation"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:111
+msgid ""
+"Flower's documentation uses `Sphinx <https://www.sphinx-doc.org/>`_. "
+"There's no convenience script to re-build the documentation yet, but it's"
+" pretty easy::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:117
+msgid "This will generate HTML documentation in ``doc/build/html``."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:2
+msgid "Example: FedBN in PyTorch - From Centralized To Federated"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:4
+msgid ""
+"This tutorial will show you how to use Flower to build a federated "
+"version of an existing machine learning workload with `FedBN "
+"<https://github.com/med-air/FedBN>`_, a federated training strategy "
+"designed for non-iid data. We are using PyTorch to train a Convolutional "
+"Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset. "
+"When applying FedBN, only few changes needed compared to `Example: "
+"PyTorch - From Centralized To Federated <https://flower.dev/docs/examples"
+"/pytorch-from-centralized-to-federated.html>`_."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:10
+msgid "Centralized Training"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10
+msgid ""
+"All files are revised based on `Example: PyTorch - From Centralized To "
+"Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"federated.html>`_. The only thing to do is modifying the file called "
+":code:`cifar.py`, revised part is shown below:"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:13
+msgid ""
+"The model architecture defined in class Net() is added with Batch "
+"Normalization layers accordingly."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:41
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:157
+msgid "You can now run your machine learning workload:"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:47
+msgid ""
+"So far this should all look fairly familiar if you've used PyTorch "
+"before. Let's take the next step and use what we've built to create a "
+"federated learning system within FedBN, the sytstem consists of one "
+"server and two clients."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:51
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:167
+msgid "Federated Training"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53
+msgid ""
+"If you have read `Example: PyTorch - From Centralized To Federated "
+"<https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"federated.html>`_, the following parts are easy to follow, onyl "
+":code:`get_parameters` and :code:`set_parameters` function in "
+":code:`client.py` needed to revise. If not, please read the `Example: "
+"PyTorch - From Centralized To Federated <https://flower.dev/docs/examples"
+"/pytorch-from-centralized-to-federated.html>`_. first."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:56
+msgid ""
+"Our example consists of one *server* and two *clients*. In FedBN, "
+":code:`server.py` keeps unchanged, we can start the server directly."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:62
+msgid ""
+"Finally, we will revise our *client* logic by changing "
+":code:`get_parameters` and :code:`set_parameters` in :code:`client.py`, "
+"we will exclude batch normalization parameters from model parameter list "
+"when sending to or receiving from the server."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:85
+msgid "Now, you can now open two additional terminal windows and run"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:91
+msgid ""
+"in each window (make sure that the server is still running before you do "
+"so) and see your (previously centralized) PyTorch project run federated "
+"learning with FedBN strategy across two clients. Congratulations!"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94
+#: ../../source/example-jax-from-centralized-to-federated.rst:277
+#: ../../source/example-mxnet-walk-through.rst:356
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:310
+#: ../../source/tutorial-quickstart-jax.rst:283
+msgid "Next Steps"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:96
+msgid ""
+"The full source code for this example can be found `here "
+"<https://github.com/adap/flower/blob/main/examples/pytorch-from-"
+"centralized-to-federated>`_. Our example is of course somewhat over-"
+"simplified because both clients load the exact same dataset, which isn't "
+"realistic. You're now prepared to explore this topic further. How about "
+"using different subsets of CIFAR-10 on each client? How about adding more"
+" clients?"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:2
+msgid "Example: JAX - Run JAX Federated"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:4
+#: ../../source/tutorial-quickstart-jax.rst:10
+msgid ""
+"This tutorial will show you how to use Flower to build a federated "
+"version of an existing JAX workload. We are using JAX to train a linear "
+"regression model on a scikit-learn dataset. We will structure the example"
+" similar to our `PyTorch - From Centralized To Federated "
+"<https://github.com/adap/flower/blob/main/examples/pytorch-from-"
+"centralized-to-federated>`_ walkthrough. First, we build a centralized "
+"training approach based on the `Linear Regression with JAX "
+"<https://coax.readthedocs.io/en/latest/examples/linear_regression/jax.html>`_"
+" tutorial`. Then, we build upon the centralized training code to run the "
+"training in a federated fashion."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:10
+#: ../../source/tutorial-quickstart-jax.rst:16
+msgid ""
+"Before we start building our JAX example, we need install the packages "
+":code:`jax`, :code:`jaxlib`, :code:`scikit-learn`, and :code:`flwr`:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:18
+#: ../../source/tutorial-quickstart-jax.rst:24
+msgid "Linear Regression with JAX"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:20
+#: ../../source/tutorial-quickstart-jax.rst:26
+msgid ""
+"We begin with a brief description of the centralized training code based "
+"on a :code:`Linear Regression` model. If you want a more in-depth "
+"explanation of what's going on then have a look at the official `JAX "
+"documentation <https://jax.readthedocs.io/>`_."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:23
+#: ../../source/tutorial-quickstart-jax.rst:29
+msgid ""
+"Let's create a new file called :code:`jax_training.py` with all the "
+"components required for a traditional (centralized) linear regression "
+"training. First, the JAX packages :code:`jax` and :code:`jaxlib` need to "
+"be imported. In addition, we need to import :code:`sklearn` since we use "
+":code:`make_regression` for the dataset and :code:`train_test_split` to "
+"split the dataset into a training and test set. You can see that we do "
+"not yet import the :code:`flwr` package for federated learning. This will"
+" be done later."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:37
+#: ../../source/tutorial-quickstart-jax.rst:43
+msgid ""
+"The :code:`load_data()` function loads the mentioned training and test "
+"sets."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:47
+#: ../../source/tutorial-quickstart-jax.rst:53
+msgid ""
+"The model architecture (a very simple :code:`Linear Regression` model) is"
+" defined in :code:`load_model()`."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:59
+#: ../../source/tutorial-quickstart-jax.rst:65
+msgid ""
+"We now need to define the training (function :code:`train()`), which "
+"loops over the training set and measures the loss (function "
+":code:`loss_fn()`) for each batch of training examples. The loss function"
+" is separate since JAX takes derivatives with a :code:`grad()` function "
+"(defined in the :code:`main()` function and called in :code:`train()`)."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:77
+#: ../../source/tutorial-quickstart-jax.rst:83
+msgid ""
+"The evaluation of the model is defined in the function "
+":code:`evaluation()`. The function takes all test examples and measures "
+"the loss of the linear regression model."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:88
+#: ../../source/tutorial-quickstart-jax.rst:94
+msgid ""
+"Having defined the data loading, model architecture, training, and "
+"evaluation we can put everything together and train our model using JAX. "
+"As already mentioned, the :code:`jax.grad()` function is defined in "
+":code:`main()` and passed to :code:`train()`."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:105
+#: ../../source/tutorial-quickstart-jax.rst:111
+msgid "You can now run your (centralized) JAX linear regression workload:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:111
+#: ../../source/tutorial-quickstart-jax.rst:117
+msgid ""
+"So far this should all look fairly familiar if you've used JAX before. "
+"Let's take the next step and use what we've built to create a simple "
+"federated learning system consisting of one server and two clients."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:115
+#: ../../source/tutorial-quickstart-jax.rst:121
+msgid "JAX meets Flower"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:117
+#: ../../source/tutorial-quickstart-jax.rst:123
+msgid ""
+"The concept of federating an existing workload is always the same and "
+"easy to understand. We have to start a *server* and then use the code in "
+":code:`jax_training.py` for the *clients* that are connected to the "
+"*server*. The *server* sends model parameters to the clients. The "
+"*clients* run the training and update the parameters. The updated "
+"parameters are sent back to the *server*, which averages all received "
+"parameter updates. This describes one round of the federated learning "
+"process, and we repeat this for multiple rounds."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:123
+#: ../../source/example-mxnet-walk-through.rst:204
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:181
+#: ../../source/tutorial-quickstart-jax.rst:129
+msgid ""
+"Our example consists of one *server* and two *clients*. Let's set up "
+":code:`server.py` first. The *server* needs to import the Flower package "
+":code:`flwr`. Next, we use the :code:`start_server` function to start a "
+"server and tell it to perform three rounds of federated learning."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:133
+#: ../../source/example-mxnet-walk-through.rst:214
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:191
+#: ../../source/tutorial-quickstart-jax.rst:139
+msgid "We can already start the *server*:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:139
+#: ../../source/tutorial-quickstart-jax.rst:145
+msgid ""
+"Finally, we will define our *client* logic in :code:`client.py` and build"
+" upon the previously defined JAX training in :code:`jax_training.py`. Our"
+" *client* needs to import :code:`flwr`, but also :code:`jax` and "
+":code:`jaxlib` to update the parameters on our JAX model:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:154
+#: ../../source/tutorial-quickstart-jax.rst:160
+msgid ""
+"Implementing a Flower *client* basically means implementing a subclass of"
+" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
+"Our implementation will be based on :code:`flwr.client.NumPyClient` and "
+"we'll call it :code:`FlowerClient`. :code:`NumPyClient` is slightly "
+"easier to implement than :code:`Client` if you use a framework with good "
+"NumPy interoperability (like JAX) because it avoids some of the "
+"boilerplate that would otherwise be necessary. :code:`FlowerClient` needs"
+" to implement four methods, two methods for getting/setting model "
+"parameters, one method for training the model, and one method for testing"
+" the model:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:161
+#: ../../source/example-mxnet-walk-through.rst:242
+#: ../../source/tutorial-quickstart-jax.rst:167
+msgid ":code:`set_parameters (optional)`"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:160
+#: ../../source/example-mxnet-walk-through.rst:241
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:219
+#: ../../source/tutorial-quickstart-jax.rst:166
+msgid ""
+"set the model parameters on the local model that are received from the "
+"server"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:161
+#: ../../source/tutorial-quickstart-jax.rst:167
+msgid "transform parameters to NumPy :code:`ndarray`'s"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:162
+#: ../../source/example-mxnet-walk-through.rst:243
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:220
+#: ../../source/tutorial-quickstart-jax.rst:168
+msgid ""
+"loop over the list of model parameters received as NumPy "
+":code:`ndarray`'s (think list of neural network layers)"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:163
+#: ../../source/example-mxnet-walk-through.rst:244
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:221
+#: ../../source/tutorial-quickstart-jax.rst:169
+#: ../../source/tutorial-quickstart-mxnet.rst:169
+#: ../../source/tutorial-quickstart-pytorch.rst:155
+#: ../../source/tutorial-quickstart-scikitlearn.rst:108
+msgid ":code:`get_parameters`"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:164
+#: ../../source/example-mxnet-walk-through.rst:245
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:222
+#: ../../source/tutorial-quickstart-jax.rst:170
+msgid ""
+"get the model parameters and return them as a list of NumPy "
+":code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:167
+#: ../../source/example-mxnet-walk-through.rst:248
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:225
+#: ../../source/tutorial-quickstart-jax.rst:173
+#: ../../source/tutorial-quickstart-mxnet.rst:175
+#: ../../source/tutorial-quickstart-pytorch.rst:161
+#: ../../source/tutorial-quickstart-scikitlearn.rst:115
+msgid ":code:`fit`"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:166
+#: ../../source/example-jax-from-centralized-to-federated.rst:170
+#: ../../source/example-mxnet-walk-through.rst:247
+#: ../../source/example-mxnet-walk-through.rst:251
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:224
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:228
+#: ../../source/tutorial-quickstart-jax.rst:172
+#: ../../source/tutorial-quickstart-jax.rst:176
+msgid ""
+"update the parameters of the local model with the parameters received "
+"from the server"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:167
+#: ../../source/example-mxnet-walk-through.rst:248
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:225
+#: ../../source/tutorial-quickstart-jax.rst:173
+msgid "train the model on the local training set"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:168
+#: ../../source/tutorial-quickstart-jax.rst:174
+msgid "get the updated local model parameters and return them to the server"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:172
+#: ../../source/example-mxnet-walk-through.rst:253
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:230
+#: ../../source/tutorial-quickstart-jax.rst:178
+#: ../../source/tutorial-quickstart-mxnet.rst:178
+#: ../../source/tutorial-quickstart-pytorch.rst:164
+#: ../../source/tutorial-quickstart-scikitlearn.rst:118
+msgid ":code:`evaluate`"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:171
+#: ../../source/example-mxnet-walk-through.rst:252
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:229
+#: ../../source/tutorial-quickstart-jax.rst:177
+msgid "evaluate the updated model on the local test set"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:172
+#: ../../source/tutorial-quickstart-jax.rst:178
+msgid "return the local loss to the server"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:174
+#: ../../source/tutorial-quickstart-jax.rst:180
+msgid ""
+"The challenging part is to transform the JAX model parameters from "
+":code:`DeviceArray` to :code:`NumPy ndarray` to make them compatible with"
+" `NumPyClient`."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:176
+#: ../../source/tutorial-quickstart-jax.rst:182
+msgid ""
+"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make"
+" use of the functions :code:`train()` and :code:`evaluate()` previously "
+"defined in :code:`jax_training.py`. So what we really do here is we tell "
+"Flower through our :code:`NumPyClient` subclass which of our already "
+"defined functions to call for training and evaluation. We included type "
+"annotations to give you a better understanding of the data types that get"
+" passed around."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:245
+#: ../../source/tutorial-quickstart-jax.rst:251
+msgid "Having defined the federation process, we can run it."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:268
+#: ../../source/example-mxnet-walk-through.rst:347
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:301
+#: ../../source/tutorial-quickstart-jax.rst:274
+msgid "And that's it. You can now open two additional terminal windows and run"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:274
+#: ../../source/tutorial-quickstart-jax.rst:280
+msgid ""
+"in each window (make sure that the server is still running before you do "
+"so) and see your JAX project run federated learning across two clients. "
+"Congratulations!"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:279
+#: ../../source/tutorial-quickstart-jax.rst:285
+msgid ""
+"The source code of this example was improved over time and can be found "
+"here: `Quickstart JAX <https://github.com/adap/flower/blob/main/examples"
+"/quickstart-jax>`_. Our example is somewhat over-simplified because both "
+"clients load the same dataset."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:282
+#: ../../source/tutorial-quickstart-jax.rst:288
+msgid ""
+"You're now prepared to explore this topic further. How about using a more"
+" sophisticated model or using a different dataset? How about adding more "
+"clients?"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:2
+msgid "Example: MXNet - Run MXNet Federated"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:4
+msgid ""
+"This tutorial will show you how to use Flower to build a federated "
+"version of an existing MXNet workload. We are using MXNet to train a "
+"Sequential model on the MNIST dataset. We will structure the example "
+"similar to our `PyTorch - From Centralized To Federated "
+"<https://github.com/adap/flower/blob/main/examples/pytorch-from-"
+"centralized-to-federated>`_ walkthrough. MXNet and PyTorch are very "
+"similar and a very good comparison between MXNet and PyTorch is given "
+"`here <https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials"
+"/getting-started/to-mxnet/pytorch.html>`_. First, we build a centralized "
+"training approach based on the `Handwritten Digit Recognition "
+"<https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_"
+" tutorial. Then, we build upon the centralized training code to run the "
+"training in a federated fashion."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:10
+msgid ""
+"Before we start setting up our MXNet example, we install the "
+":code:`mxnet` and :code:`flwr` packages:"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:19
+msgid "MNIST Training with MXNet"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:21
+msgid ""
+"We begin with a brief description of the centralized training code based "
+"on a :code:`Sequential` model. If you want a more in-depth explanation of"
+" what's going on then have a look at the official `MXNet tutorial "
+"<https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/>`_."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:24
+msgid ""
+"Let's create a new file called:code:`mxnet_mnist.py` with all the "
+"components required for a traditional (centralized) MNIST training. "
+"First, the MXNet package :code:`mxnet` needs to be imported. You can see "
+"that we do not yet import the :code:`flwr` package for federated "
+"learning. This will be done later."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:42
+msgid "The :code:`load_data()` function loads the MNIST training and test sets."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:57
+msgid ""
+"As already mentioned, we will use the MNIST dataset for this machine "
+"learning workload. The model architecture (a very simple "
+":code:`Sequential` model) is defined in :code:`model()`."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:70
+msgid ""
+"We now need to define the training (function :code:`train()`) which loops"
+" over the training set and measures the loss for each batch of training "
+"examples."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:123
+msgid ""
+"The evaluation of the model is defined in function :code:`test()`. The "
+"function loops over all test samples and measures the loss and accuracy "
+"of the model based on the test dataset."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:158
+msgid ""
+"Having defined the data loading, model architecture, training, and "
+"evaluation we can put everything together and train our model on MNIST. "
+"Note that the GPU/CPU device for the training and testing is defined "
+"within the :code:`ctx` (context)."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:184
+msgid "You can now run your (centralized) MXNet machine learning workload:"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:190
+msgid ""
+"So far this should all look fairly familiar if you've used MXNet (or even"
+" PyTorch) before. Let's take the next step and use what we've built to "
+"create a simple federated learning system consisting of one server and "
+"two clients."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:194
+msgid "MXNet meets Flower"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:196
+msgid ""
+"So far, it was not easily possible to use MXNet workloads for federated "
+"learning because federated learning is not supported in MXNet. Since "
+"Flower is fully agnostic towards the underlying machine learning "
+"framework, it can be used to federated arbitrary machine learning "
+"workloads. This section will show you how Flower can be used to federate "
+"our centralized MXNet workload."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:198
+msgid ""
+"The concept to federate an existing workload is always the same and easy "
+"to understand. We have to start a *server* and then use the code in "
+":code:`mxnet_mnist.py` for the *clients* that are connected to the "
+"*server*. The *server* sends model parameters to the clients. The "
+"*clients* run the training and update the parameters. The updated "
+"parameters are sent back to the *server* which averages all received "
+"parameter updates. This describes one round of the federated learning "
+"process and we repeat this for multiple rounds."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:220
+msgid ""
+"Finally, we will define our *client* logic in :code:`client.py` and build"
+" upon the previously defined MXNet training in :code:`mxnet_mnist.py`. "
+"Our *client* needs to import :code:`flwr`, but also :code:`mxnet` to "
+"update the parameters on our MXNet model:"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:235
+msgid ""
+"Implementing a Flower *client* basically means implementing a subclass of"
+" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
+"Our implementation will be based on :code:`flwr.client.NumPyClient` and "
+"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slighly easier "
+"to implement than :code:`Client` if you use a framework with good NumPy "
+"interoperability (like PyTorch or MXNet) because it avoids some of the "
+"boilerplate that would otherwise be necessary. :code:`MNISTClient` needs "
+"to implement four methods, two methods for getting/setting model "
+"parameters, one method for training the model, and one method for testing"
+" the model:"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:242
+msgid "transform MXNet :code:`NDArray`'s to NumPy :code:`ndarray`'s"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:249
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:226
+msgid "get the updated local model weights and return them to the server"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:253
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:230
+msgid "return the local loss and accuracy to the server"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:255
+msgid ""
+"The challenging part is to transform the MXNet parameters from "
+":code:`NDArray` to :code:`NumPy Arrays` to make it readable for Flower."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:257
+msgid ""
+"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make"
+" use of the functions :code:`train()` and :code:`test()` previously "
+"defined in :code:`mxnet_mnist.py`. So what we really do here is we tell "
+"Flower through our :code:`NumPyClient` subclass which of our already "
+"defined functions to call for training and evaluation. We included type "
+"annotations to give you a better understanding of the data types that get"
+" passed around."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:319
+msgid ""
+"Having defined data loading, model architecture, training, and evaluation"
+" we can put everything together and train our :code:`Sequential` model on"
+" MNIST."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:353
+msgid ""
+"in each window (make sure that the server is still running before you do "
+"so) and see your MXNet project run federated learning across two clients."
+" Congratulations!"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:358
+msgid ""
+"The full source code for this example: `MXNet: From Centralized To "
+"Federated (Code) <https://github.com/adap/flower/blob/main/examples"
+"/mxnet-from-centralized-to-federated>`_. Our example is of course "
+"somewhat over-simplified because both clients load the exact same "
+"dataset, which isn't realistic. You're now prepared to explore this topic"
+" further. How about using a CNN or using a different dataset? How about "
+"adding more clients?"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:2
+msgid "Example: PyTorch - From Centralized To Federated"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:4
+msgid ""
+"This tutorial will show you how to use Flower to build a federated "
+"version of an existing machine learning workload. We are using PyTorch to"
+" train a Convolutional Neural Network on the CIFAR-10 dataset. First, we "
+"introduce this machine learning task with a centralized training approach"
+" based on the `Deep Learning with PyTorch "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_ "
+"tutorial. Then, we build upon the centralized training code to run the "
+"training in a federated fashion."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:12
+msgid ""
+"We begin with a brief description of the centralized CNN training code. "
+"If you want a more in-depth explanation of what's going on then have a "
+"look at the official `PyTorch tutorial "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:15
+msgid ""
+"Let's create a new file called :code:`cifar.py` with all the components "
+"required for a traditional (centralized) training on CIFAR-10. First, all"
+" required packages (such as :code:`torch` and :code:`torchvision`) need "
+"to be imported. You can see that we do not import any package for "
+"federated learning. You can keep all these imports as they are even when "
+"we add the federated learning components at a later point."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:32
+msgid ""
+"As already mentioned we will use the CIFAR-10 dataset for this machine "
+"learning workload. The model architecture (a very simple Convolutional "
+"Neural Network) is defined in :code:`class Net()`."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:56
+msgid ""
+"The :code:`load_data()` function loads the CIFAR-10 training and test "
+"sets. The :code:`transform` normalized the data after loading."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:74
+msgid ""
+"We now need to define the training (function :code:`train()`) which loops"
+" over the training set, measures the loss, backpropagates it, and then "
+"takes one optimizer step for each batch of training examples."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:76
+msgid ""
+"The evaluation of the model is defined in the function :code:`test()`. "
+"The function loops over all test samples and measures the loss of the "
+"model based on the test dataset."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:136
+msgid ""
+"Having defined the data loading, model architecture, training, and "
+"evaluation we can put everything together and train our CNN on CIFAR-10."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:163
+msgid ""
+"So far, this should all look fairly familiar if you've used PyTorch "
+"before. Let's take the next step and use what we've built to create a "
+"simple federated learning system consisting of one server and two "
+"clients."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:169
+msgid ""
+"The simple machine learning project discussed in the previous section "
+"trains the model on a single dataset (CIFAR-10), we call this centralized"
+" learning. This concept of centralized learning, as shown in the previous"
+" section, is probably known to most of you, and many of you have used it "
+"previously. Normally, if you'd want to run machine learning workloads in "
+"a federated fashion, then you'd have to change most of your code and set "
+"everything up from scratch. This can be a considerable effort."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:173
+msgid ""
+"However, with Flower you can evolve your pre-existing code into a "
+"federated learning setup without the need for a major rewrite."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:175
+msgid ""
+"The concept is easy to understand. We have to start a *server* and then "
+"use the code in :code:`cifar.py` for the *clients* that are connected to "
+"the *server*. The *server* sends model parameters to the clients. The "
+"*clients* run the training and update the paramters. The updated "
+"parameters are sent back to the *server* which averages all received "
+"parameter updates. This describes one round of the federated learning "
+"process and we repeat this for multiple rounds."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:197
+msgid ""
+"Finally, we will define our *client* logic in :code:`client.py` and build"
+" upon the previously defined centralized training in :code:`cifar.py`. "
+"Our *client* needs to import :code:`flwr`, but also :code:`torch` to "
+"update the paramters on our PyTorch model:"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:213
+msgid ""
+"Implementing a Flower *client* basically means implementing a subclass of"
+" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
+"Our implementation will be based on :code:`flwr.client.NumPyClient` and "
+"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slighly easier "
+"to implement than :code:`Client` if you use a framework with good NumPy "
+"interoperability (like PyTorch or TensorFlow/Keras) because it avoids "
+"some of the boilerplate that would otherwise be necessary. "
+":code:`CifarClient` needs to implement four methods, two methods for "
+"getting/setting model parameters, one method for training the model, and "
+"one method for testing the model:"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:219
+msgid ":code:`set_parameters`"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:232
+msgid ""
+"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make"
+" use of the functions :code:`train()` and :code:`test()` previously "
+"defined in :code:`cifar.py`. So what we really do here is we tell Flower "
+"through our :code:`NumPyClient` subclass which of our already defined "
+"functions to call for training and evaluation. We included type "
+"annotations to give you a better understanding of the data types that get"
+" passed around."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:280
+msgid ""
+"All that's left to do it to define a function that loads both model and "
+"data, creates a :code:`CifarClient`, and starts this client. You load "
+"your data and model by using :code:`cifar.py`. Start :code:`CifarClient` "
+"with the function :code:`fl.client.start_numpy_client()` by pointing it "
+"at the same IP adress we used in :code:`server.py`:"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:307
+msgid ""
+"in each window (make sure that the server is running before you do so) "
+"and see your (previously centralized) PyTorch project run federated "
+"learning across two clients. Congratulations!"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:312
+msgid ""
+"The full source code for this example: `PyTorch: From Centralized To "
+"Federated (Code) <https://github.com/adap/flower/blob/main/examples"
+"/pytorch-from-centralized-to-federated>`_. Our example is, of course, "
+"somewhat over-simplified because both clients load the exact same "
+"dataset, which isn't realistic. You're now prepared to explore this topic"
+" further. How about using different subsets of CIFAR-10 on each client? "
+"How about adding more clients?"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:2
+msgid "Example: Walk-Through PyTorch & MNIST"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:4
+msgid ""
+"In this tutorial we will learn, how to train a Convolutional Neural "
+"Network on MNIST using Flower and PyTorch."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:6
+#: ../../source/tutorial-quickstart-mxnet.rst:14
+#: ../../source/tutorial-quickstart-pytorch.rst:17
+#: ../../source/tutorial-quickstart-scikitlearn.rst:14
+msgid ""
+"Our example consists of one *server* and two *clients* all having the "
+"same model."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:8
+#: ../../source/tutorial-quickstart-pytorch.rst:19
+msgid ""
+"*Clients* are responsible for generating individual weight-updates for "
+"the model based on their local datasets. These updates are then sent to "
+"the *server* which will aggregate them to produce a better model. "
+"Finally, the *server* sends this improved version of the model back to "
+"each *client*. A complete cycle of weight updates is called a *round*."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:12
+#: ../../source/tutorial-quickstart-pytorch.rst:23
+msgid ""
+"Now that we have a rough idea of what is going on, let's get started. We "
+"first need to install Flower. You can do this by running :"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:18
+msgid ""
+"Since we want to use PyTorch to solve a computer vision task, let's go "
+"ahead an install PyTorch and the **torchvision** library:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:26
+msgid "Ready... Set... Train!"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:28
+msgid ""
+"Now that we have all our dependencies installed, let's run a simple "
+"distributed training with two clients and one server. Our training "
+"procedure and network architecture are based on PyTorch's `Basic MNIST "
+"Example <https://github.com/pytorch/examples/tree/master/mnist>`_. This "
+"will allow you see how easy it is to wrap your code with Flower and begin"
+" training in a federated way. We provide you with two helper scripts, "
+"namely *run-server.sh*, and *run-clients.sh*. Don't be afraid to look "
+"inside, they are simple enough =)."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:31
+msgid ""
+"Go ahead and launch on a terminal the *run-server.sh* script first as "
+"follows:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:38
+msgid "Now that the server is up and running, go ahead and launch the clients."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:45
+msgid ""
+"Et voilà! You should be seeing the training procedure and, after a few "
+"iterations, the test accuracy for each client."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:66
+msgid "Now, let's see what is really happening inside."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:69
+#: ../../source/tutorial-quickstart-ios.rst:129
+#: ../../source/tutorial-quickstart-mxnet.rst:224
+#: ../../source/tutorial-quickstart-pytorch.rst:203
+#: ../../source/tutorial-quickstart-scikitlearn.rst:157
+#: ../../source/tutorial-quickstart-tensorflow.rst:98
+#: ../../source/tutorial-quickstart-xgboost.rst:306
+msgid "Flower Server"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:71
+msgid ""
+"Inside the server helper script *run-server.sh* you will find the "
+"following code that basically runs the :code:`server.py`"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:78
+msgid ""
+"We can go a bit deeper and see that :code:`server.py` simply launches a "
+"server that will coordinate three rounds of training. Flower Servers are "
+"very customizable, but for simple workloads, we can start a server using "
+"the :ref:`start_server <flwr-server-start_server-apiref>` function and "
+"leave all the configuration possibilities at their default values, as "
+"seen below."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:89
+#: ../../source/tutorial-quickstart-ios.rst:34
+#: ../../source/tutorial-quickstart-mxnet.rst:34
+#: ../../source/tutorial-quickstart-pytorch.rst:37
+#: ../../source/tutorial-quickstart-scikitlearn.rst:40
+#: ../../source/tutorial-quickstart-tensorflow.rst:29
+#: ../../source/tutorial-quickstart-xgboost.rst:52
+msgid "Flower Client"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:91
+msgid ""
+"Next, let's take a look at the *run-clients.sh* file. You will see that "
+"it contains the main loop that starts a set of *clients*."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:100
+msgid ""
+"**cid**: is the client ID. It is an integer that uniquely identifies "
+"client identifier."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:101
+msgid "**sever_address**: String that identifies IP and port of the server."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:102
+msgid ""
+"**nb_clients**: This defines the number of clients being created. This "
+"piece of information is not required by the client, but it helps us "
+"partition the original MNIST dataset to make sure that every client is "
+"working on unique subsets of both *training* and *test* sets."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:104
+msgid ""
+"Again, we can go deeper and look inside :code:`flwr_example/quickstart-"
+"pytorch/client.py`. After going through the argument parsing code at the "
+"beginning of our :code:`main` function, you will find a call to "
+":code:`mnist.load_data`. This function is responsible for partitioning "
+"the original MNIST datasets (*training* and *test*) and returning a "
+":code:`torch.utils.data.DataLoader` s for each of them. We then "
+"instantiate a :code:`PytorchMNISTClient` object with our client ID, our "
+"DataLoaders, the number of epochs in each round, and which device we want"
+" to use for training (CPU or GPU)."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:119
+msgid ""
+"The :code:`PytorchMNISTClient` object when finally passed to "
+":code:`fl.client.start_client` along with the server's address as the "
+"training process begins."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:123
+msgid "A Closer Look"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:125
+msgid ""
+"Now, let's look closely into the :code:`PytorchMNISTClient` inside "
+":code:`flwr_example.quickstart-pytorch.mnist` and see what it is doing:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:226
+msgid ""
+"The first thing to notice is that :code:`PytorchMNISTClient` instantiates"
+" a CNN model inside its constructor"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:244
+msgid ""
+"The code for the CNN is available under :code:`quickstart-pytorch.mnist` "
+"and it is reproduced below. It is the same network found in `Basic MNIST "
+"Example <https://github.com/pytorch/examples/tree/master/mnist>`_."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:290
+msgid ""
+"The second thing to notice is that :code:`PytorchMNISTClient` class "
+"inherits from the :code:`fl.client.Client`, and hence it must implement "
+"the following methods:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:315
+msgid ""
+"When comparing the abstract class to its derived class "
+":code:`PytorchMNISTClient` you will notice that :code:`fit` calls a "
+":code:`train` function and that :code:`evaluate` calls a :code:`test`: "
+"function."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:317
+msgid ""
+"These functions can both be found inside the same :code:`quickstart-"
+"pytorch.mnist` module:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:437
+msgid ""
+"Observe that these functions encapsulate regular training and test loops "
+"and provide :code:`fit` and :code:`evaluate` with final statistics for "
+"each round. You could substitute them with your custom train and test "
+"loops and change the network architecture, and the entire example would "
+"still work flawlessly. As a matter of fact, why not try and modify the "
+"code to an example of your liking?"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:444
+msgid "Give It a Try"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:445
+msgid ""
+"Looking through the quickstart code description above will have given a "
+"good understanding of how *clients* and *servers* work in Flower, how to "
+"run a simple experiment, and the internals of a client wrapper. Here are "
+"a few things you could try on your own and get more experience with "
+"Flower:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:448
+msgid ""
+"Try and change :code:`PytorchMNISTClient` so it can accept different "
+"architectures."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:449
+msgid "Modify the :code:`train` function so that it accepts different optimizers"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:450
+msgid ""
+"Modify the :code:`test` function so that it proves not only the top-1 "
+"(regular accuracy) but also the top-5 accuracy?"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:451
+msgid ""
+"Go larger! Try to adapt the code to larger images and datasets. Why not "
+"try training on ImageNet with a ResNet-50?"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:453
+msgid "You are ready now. Enjoy learning in a federated way!"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:2
+msgid "Differential privacy"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:4
+msgid ""
+"Flower provides differential privacy (DP) wrapper classes for the easy "
+"integration of the central DP guarantees provided by DP-FedAvg into "
+"training pipelines defined in any of the various ML frameworks that "
+"Flower is compatible with."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:7
+msgid ""
+"Please note that these components are still experimental, the correct "
+"configuration of DP for a specific task is still an unsolved problem."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:10
+msgid ""
+"The name DP-FedAvg is misleading since it can be applied on top of any FL"
+" algorithm that conforms to the general structure prescribed by the "
+"FedOpt family of algorithms."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:13
+msgid "DP-FedAvg"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:15
+msgid ""
+"DP-FedAvg, originally proposed by McMahan et al. [mcmahan]_ and extended "
+"by Andrew et al. [andrew]_, is essentially FedAvg with the following "
+"modifications."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:17
+msgid ""
+"**Clipping** : The influence of each client's update is bounded by "
+"clipping it. This is achieved by enforcing a cap on the L2 norm of the "
+"update, scaling it down if needed."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:18
+msgid ""
+"**Noising** :  Gaussian noise, calibrated to the clipping threshold, is "
+"added to the average computed at the server."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:20
+msgid ""
+"The distribution of the update norm has been shown to vary from task-to-"
+"task and to evolve as training progresses. Therefore, we use an adaptive "
+"approach [andrew]_ that continuously adjusts the clipping threshold to "
+"track a prespecified quantile of the update norm distribution."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:23
+msgid "Simplifying Assumptions"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:25
+msgid ""
+"We make (and attempt to enforce) a number of assumptions that must be "
+"satisfied to ensure that the training process actually realises the "
+":math:`(\\epsilon, \\delta)` guarantees the user has in mind when "
+"configuring the setup."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:27
+msgid ""
+"**Fixed-size subsampling** :Fixed-size subsamples of the clients must be "
+"taken at each round, as opposed to variable-sized Poisson subsamples."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:28
+msgid ""
+"**Unweighted averaging** : The contributions from all the clients must "
+"weighted equally in the aggregate to eliminate the requirement for the "
+"server to know in advance the sum of the weights of all clients available"
+" for selection."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:29
+msgid ""
+"**No client failures** : The set of available clients must stay constant "
+"across all rounds of training. In other words, clients cannot drop out or"
+" fail."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:31
+msgid ""
+"The first two are useful for eliminating a multitude of complications "
+"associated with calibrating the noise to the clipping threshold while the"
+" third one is required to comply with the assumptions of the privacy "
+"analysis."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:34
+msgid ""
+"These restrictions are in line with constraints imposed by Andrew et al. "
+"[andrew]_."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:37
+msgid "Customizable Responsibility for Noise injection"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:38
+msgid ""
+"In contrast to other implementations where the addition of noise is "
+"performed at the server, you can configure the site of noise injection to"
+" better match your threat model. We provide users with the flexibility to"
+" set up the training such that each client independently adds a small "
+"amount of noise to the clipped update, with the result that simply "
+"aggregating the noisy updates is equivalent to the explicit addition of "
+"noise to the non-noisy aggregate at the server."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:41
+msgid ""
+"To be precise, if we let :math:`m` be the number of clients sampled each "
+"round and :math:`\\sigma_\\Delta` be the scale of the total Gaussian "
+"noise that needs to be added to the sum of the model updates, we can use "
+"simple maths to show that this is equivalent to each client adding noise "
+"with scale :math:`\\sigma_\\Delta/\\sqrt{m}`."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:44
+msgid "Wrapper-based approach"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:46
+msgid ""
+"Introducing DP to an existing workload can be thought of as adding an "
+"extra layer of security around it. This inspired us to provide the "
+"additional server and client-side logic needed to make the training "
+"process differentially private as wrappers for instances of the "
+":code:`Strategy` and :code:`NumPyClient` abstract classes respectively. "
+"This wrapper-based approach has the advantage of being easily composable "
+"with other wrappers that someone might contribute to the Flower library "
+"in the future, e.g., for secure aggregation. Using Inheritance instead "
+"can be tedious because that would require the creation of new sub- "
+"classes every time a new class implementing :code:`Strategy` or "
+":code:`NumPyClient` is defined."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:49
+msgid "Server-side logic"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:51
+msgid ""
+"The first version of our solution was to define a decorator whose "
+"constructor accepted, among other things, a boolean valued variable "
+"indicating whether adaptive clipping was to be enabled or not. We quickly"
+" realized that this would clutter its :code:`__init__()` function with "
+"variables corresponding to hyperparameters of adaptive clipping that "
+"would remain unused when it was disabled. A cleaner implementation could "
+"be achieved by splitting the functionality into two decorators, "
+":code:`DPFedAvgFixed` and :code:`DPFedAvgAdaptive`, with the latter sub- "
+"classing the former. The constructors for both classes accept a boolean "
+"parameter :code:`server_side_noising`, which, as the name suggests, "
+"determines where noising is to be performed."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:54
+msgid "DPFedAvgFixed"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:56
+msgid ""
+"The server-side capabilities required for the original version of DP-"
+"FedAvg, i.e., the one which performed fixed clipping, can be completely "
+"captured with the help of wrapper logic for just the following two "
+"methods of the :code:`Strategy` abstract class."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:58
+msgid ""
+":code:`configure_fit()` : The config dictionary being sent by the wrapped"
+" :code:`Strategy` to each client needs to be augmented with an additional"
+" value equal to the clipping threshold (keyed under "
+":code:`dpfedavg_clip_norm`) and, if :code:`server_side_noising=true`, "
+"another one equal to the scale of the Gaussian noise that needs to be "
+"added at the client (keyed under :code:`dpfedavg_noise_stddev`). This "
+"entails *post*-processing of the results returned by the wrappee's "
+"implementation of :code:`configure_fit()`."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:59
+msgid ""
+":code:`aggregate_fit()`: We check whether any of the sampled clients "
+"dropped out or failed to upload an update before the round timed out. In "
+"that case, we need to abort the current round, discarding any successful "
+"updates that were received, and move on to the next one. On the other "
+"hand, if all clients responded successfully, we must force the averaging "
+"of the updates to happen in an unweighted manner by intercepting the "
+":code:`parameters` field of :code:`FitRes` for each received update and "
+"setting it to 1. Furthermore, if :code:`server_side_noising=true`, each "
+"update is perturbed with an amount of noise equal to what it would have "
+"been subjected to had client-side noising being enabled.  This entails "
+"*pre*-processing of the arguments to this method before passing them on "
+"to the wrappee's implementation of :code:`aggregate_fit()`."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:62
+msgid ""
+"We can't directly change the aggregation function of the wrapped strategy"
+" to force it to add noise to the aggregate, hence we simulate client-side"
+" noising to implement server-side noising."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:64
+msgid ""
+"These changes have been put together into a class called "
+":code:`DPFedAvgFixed`, whose constructor accepts the strategy being "
+"decorated, the clipping threshold and the number of clients sampled every"
+" round as compulsory arguments. The user is expected to specify the "
+"clipping threshold since the order of magnitude of the update norms is "
+"highly dependent on the model being trained and providing a default value"
+" would be misleading. The number of clients sampled at every round is "
+"required to calculate the amount of noise that must be added to each "
+"individual update, either by the server or the clients."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:67
+msgid "DPFedAvgAdaptive"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:69
+msgid ""
+"The additional functionality required to facilitate adaptive clipping has"
+" been provided in :code:`DPFedAvgAdaptive`, a subclass of "
+":code:`DPFedAvgFixed`. It overrides the above-mentioned methods to do the"
+" following."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:71
+msgid ""
+":code:`configure_fit()` : It intercepts the config dict returned by "
+":code:`super.configure_fit()` to add the key-value pair "
+":code:`dpfedavg_adaptive_clip_enabled:True` to it, which the client "
+"interprets as an instruction to include an indicator bit (1 if update "
+"norm <= clipping threshold, 0 otherwise) in the results returned by it."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:73
+msgid ""
+":code:`aggregate_fit()` : It follows a call to "
+":code:`super.aggregate_fit()` with one to :code:`__update_clip_norm__()`,"
+" a procedure which adjusts the clipping threshold on the basis of the "
+"indicator bits received from the sampled clients."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:77
+msgid "Client-side logic"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:79
+msgid ""
+"The client-side capabilities required can be completely captured through "
+"wrapper logic for just the :code:`fit()` method of the "
+":code:`NumPyClient` abstract class. To be precise, we need to *post-"
+"process* the update computed by the wrapped client to clip it, if "
+"necessary, to the threshold value supplied by the server as part of the "
+"config dictionary. In addition to this, it may need to perform some extra"
+" work if either (or both) of the following keys are also present in the "
+"dict."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:81
+msgid ""
+":code:`dpfedavg_noise_stddev` : Generate and add the specified amount of "
+"noise to the clipped update."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:82
+msgid ""
+":code:`dpfedavg_adaptive_clip_enabled` : Augment the metrics dict in the "
+":code:`FitRes` object being returned to the server with an indicator bit,"
+" calculated as described earlier."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:86
+msgid "Performing the :math:`(\\epsilon, \\delta)` analysis"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:88
+msgid ""
+"Assume you have trained for :math:`n` rounds with sampling fraction "
+":math:`q` and noise multiplier :math:`z`. In order to calculate the "
+":math:`\\epsilon` value this would result in for a particular "
+":math:`\\delta`, the following script may be used."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:98
+msgid ""
+"McMahan, H. Brendan, et al. \"Learning differentially private recurrent "
+"language models.\" arXiv preprint arXiv:1710.06963 (2017)."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:100
+msgid ""
+"Andrew, Galen, et al. \"Differentially private learning with adaptive "
+"clipping.\" Advances in Neural Information Processing Systems 34 (2021): "
+"17455-17466."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:2
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:292
+msgid "Federated evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:4
+msgid ""
+"There are two main approaches to evaluating models in federated learning "
+"systems: centralized (or server-side) evaluation and federated (or "
+"client-side) evaluation."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:8
+msgid "Centralized Evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:11
+msgid "Built-In Strategies"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:13
+msgid ""
+"All built-in strategies support centralized evaluation by providing an "
+"evaluation function during initialization. An evaluation function is any "
+"function that can take the current global model parameters as input and "
+"return evaluation results:"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:58
+msgid "Custom Strategies"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:60
+msgid ""
+"The :code:`Strategy` abstraction provides a method called "
+":code:`evaluate` that can directly be used to evaluate the current global"
+" model parameters. The current server implementation calls "
+":code:`evaluate` after parameter aggregation and before federated "
+"evaluation (see next paragraph)."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:65
+msgid "Federated Evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:68
+msgid "Implementing Federated Evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:70
+msgid ""
+"Client-side evaluation happens in the :code:`Client.evaluate` method and "
+"can be configured from the server side."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:101
+msgid "Configuring Federated Evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:103
+msgid ""
+"Federated evaluation can be configured from the server side. Built-in "
+"strategies support the following arguments:"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:105
+msgid ""
+":code:`fraction_evaluate`: a :code:`float` defining the fraction of "
+"clients that will be selected for evaluation. If "
+":code:`fraction_evaluate` is set to :code:`0.1` and :code:`100` clients "
+"are connected to the server, then :code:`10` will be randomly selected "
+"for evaluation. If :code:`fraction_evaluate` is set to :code:`0.0`, "
+"federated evaluation will be disabled."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:106
+msgid ""
+":code:`min_evaluate_clients`: an :code:`int`: the minimum number of "
+"clients to be selected for evaluation. If :code:`fraction_evaluate` is "
+"set to :code:`0.1`, :code:`min_evaluate_clients` is set to 20, and "
+":code:`100` clients are connected to the server, then :code:`20` clients "
+"will be selected for evaluation."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:107
+msgid ""
+":code:`min_available_clients`: an :code:`int` that defines the minimum "
+"number of clients which need to be connected to the server before a round"
+" of federated evaluation can start. If fewer than "
+":code:`min_available_clients` are connected to the server, the server "
+"will wait until more clients are connected before it continues to sample "
+"clients for evaluation."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:108
+msgid ""
+":code:`on_evaluate_config_fn`: a function that returns a configuration "
+"dictionary which will be sent to the selected clients. The function will "
+"be called during each round and provides a convenient way to customize "
+"client-side evaluation from the server side, for example, to configure "
+"the number of validation steps performed."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:135
+msgid "Evaluating Local Model Updates During Training"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:137
+msgid ""
+"Model parameters can also be evaluated during training. "
+":code:`Client.fit` can return arbitrary evaluation results as a "
+"dictionary:"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:177
+msgid "Full Code Example"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:179
+msgid ""
+"For a full code example that uses both centralized and federated "
+"evaluation, see the *Advanced TensorFlow Example* (the same approach can "
+"be applied to workloads implemented in any other framework): "
+"https://github.com/adap/flower/tree/main/examples/advanced-tensorflow"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:10
+msgid "FED Template"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:12
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:12
+msgid "Table of Contents"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:14
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:14
+msgid "[Table of Contents](#table-of-contents)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:15
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:15
+msgid "[Summary](#summary)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:16
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:16
+msgid "[Motivation](#motivation)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:17
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:17
+msgid "[Goals](#goals)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:18
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:18
+msgid "[Non-Goals](#non-goals)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:19
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:19
+msgid "[Proposal](#proposal)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:20
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:23
+msgid "[Drawbacks](#drawbacks)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:21
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:24
+msgid "[Alternatives Considered](#alternatives-considered)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:22
+msgid "[Appendix](#appendix)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:24
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:28
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:76
+msgid "Summary"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:26
+msgid "\\[TODO - sentence 1: summary of the problem\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:28
+msgid "\\[TODO - sentence 2: summary of the solution\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:30
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:47
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:77
+msgid "Motivation"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:32
+#: ../../source/fed/0000-20200102-fed-template.md:36
+#: ../../source/fed/0000-20200102-fed-template.md:40
+#: ../../source/fed/0000-20200102-fed-template.md:44
+#: ../../source/fed/0000-20200102-fed-template.md:48
+#: ../../source/fed/0000-20200102-fed-template.md:54
+#: ../../source/fed/0000-20200102-fed-template.md:58
+msgid "\\[TODO\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:34
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:53
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:78
+msgid "Goals"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:38
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:59
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:79
+msgid "Non-Goals"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:42
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:65
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:80
+msgid "Proposal"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:46
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:85
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:129
+msgid "Drawbacks"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:50
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:86
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:135
+msgid "Alternatives Considered"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:52
+msgid "\\[Alternative 1\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:56
+msgid "\\[Alternative 2\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:60
+msgid "Appendix"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:10
+msgid "Flower Enhancement Doc"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:20
+msgid "[Enhancement Doc Template](#enhancement-doc-template)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:21
+msgid "[Metadata](#metadata)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:22
+msgid "[Workflow](#workflow)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:25
+msgid "[GitHub Issues](#github-issues)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:26
+msgid "[Google Docs](#google-docs)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:30
+msgid "A Flower Enhancement is a standardized development process to"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:32
+msgid "provide a common structure for proposing larger changes"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:33
+msgid "ensure that the motivation for a change is clear"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:34
+msgid "persist project information in a version control system"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:35
+msgid "document the motivation for impactful user-facing changes"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:36
+msgid "reserve GitHub issues for tracking work in flight"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:37
+msgid ""
+"ensure community participants can successfully drive changes to "
+"completion across one or more releases while stakeholders are adequately "
+"represented throughout the process"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:39
+msgid "Hence, an Enhancement Doc combines aspects of"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:41
+msgid "a feature, and effort-tracking document"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:42
+msgid "a product requirements document"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:43
+msgid "a design document"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:45
+msgid ""
+"into one file, which is created incrementally in collaboration with the "
+"community."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:49
+msgid ""
+"For far-fetching changes or features proposed to Flower, an abstraction "
+"beyond a single GitHub issue or pull request is required to understand "
+"and communicate upcoming changes to the project."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:51
+msgid ""
+"The purpose of this process is to reduce the amount of \"tribal "
+"knowledge\" in our community. By moving decisions from Slack threads, "
+"video calls, and hallway conversations into a well-tracked artifact, this"
+" process aims to enhance communication and discoverability."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:55
+msgid ""
+"Roughly any larger, user-facing enhancement should follow the Enhancement"
+" process. If an enhancement would be described in either written or "
+"verbal communication to anyone besides the author or developer, then "
+"consider creating an Enhancement Doc."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:57
+msgid ""
+"Similarly, any technical effort (refactoring, major architectural change)"
+" that will impact a large section of the development community should "
+"also be communicated widely. The Enhancement process is suited for this "
+"even if it will have zero impact on the typical user or operator."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:61
+msgid ""
+"For small changes and additions, going through the Enhancement process "
+"would be time-consuming and unnecessary. This includes, for example, "
+"adding new Federated Learning algorithms, as these only add features "
+"without changing how Flower works or is used."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:63
+msgid ""
+"Enhancements are different from feature requests, as they are already "
+"providing a laid-out path for implementation and are championed by "
+"members of the community."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:67
+msgid ""
+"An Enhancement is captured in a Markdown file that follows a defined "
+"template and a workflow to review and store enhancement docs for "
+"reference — the Enhancement Doc."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:69
+msgid "Enhancement Doc Template"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:71
+msgid ""
+"Each enhancement doc is provided as a Markdown file having the following "
+"structure"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:73
+msgid "Metadata (as [described below](#metadata) in form of a YAML preamble)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:74
+msgid "Title (same as in metadata)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:75
+msgid "Table of Contents (if needed)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:81
+msgid "Notes/Constraints/Caveats (optional)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:82
+msgid "Design Details (optional)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:83
+msgid "Graduation Criteria"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:84
+msgid "Upgrade/Downgrade Strategy (if applicable)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:88
+msgid "As a reference, this document follows the above structure."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:90
+msgid "Metadata"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:92
+msgid ""
+"**fed-number** (Required) The `fed-number` of the last Flower Enhancement"
+" Doc + 1. With this number, it becomes easy to reference other proposals."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:94
+msgid "**title** (Required) The title of the proposal in plain language."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:96
+msgid ""
+"**status** (Required) The current status of the proposal. See "
+"[workflow](#workflow) for the possible states."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:98
+msgid ""
+"**authors** (Required) A list of authors of the proposal. This is simply "
+"the GitHub ID."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:100
+msgid ""
+"**creation-date** (Required) The date that the proposal was first "
+"submitted in a PR."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:102
+msgid ""
+"**last-updated** (Optional) The date that the proposal was last changed "
+"significantly."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:104
+msgid ""
+"**see-also** (Optional) A list of other proposals that are relevant to "
+"this one."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:106
+msgid "**replaces** (Optional) A list of proposals that this one replaces."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:108
+msgid "**superseded-by** (Optional) A list of proposals that this one supersedes."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:111
+msgid "Workflow"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:113
+msgid ""
+"The idea forming the enhancement should already have been discussed or "
+"pitched in the community. As such, it needs a champion, usually the "
+"author, who shepherds the enhancement. This person also has to find "
+"committers to Flower willing to review the proposal."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:115
+msgid ""
+"New enhancements are checked in with a file name in the form of `NNNN-"
+"YYYYMMDD-enhancement-title.md`, with `NNNN` being the Flower Enhancement "
+"Doc number, to `enhancements`. All enhancements start in `provisional` "
+"state as part of a pull request. Discussions are done as part of the pull"
+" request review."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:117
+msgid ""
+"Once an enhancement has been reviewed and approved, its status is changed"
+" to `implementable`. The actual implementation is then done in separate "
+"pull requests. These pull requests should mention the respective "
+"enhancement as part of their description. After the implementation is "
+"done, the proposal status is changed to `implemented`."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:119
+msgid ""
+"Under certain conditions, other states are possible. An Enhancement has "
+"the following states:"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:121
+msgid ""
+"`provisional`: The enhancement has been proposed and is actively being "
+"defined. This is the starting state while the proposal is being fleshed "
+"out and actively defined and discussed."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:122
+msgid "`implementable`: The enhancement has been reviewed and approved."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:123
+msgid ""
+"`implemented`: The enhancement has been implemented and is no longer "
+"actively changed."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:124
+msgid "`deferred`: The enhancement is proposed but not actively being worked on."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:125
+msgid ""
+"`rejected`: The authors and reviewers have decided that this enhancement "
+"is not moving forward."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:126
+msgid "`withdrawn`: The authors have withdrawn the enhancement."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:127
+msgid "`replaced`: The enhancement has been replaced by a new enhancement."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:131
+msgid ""
+"Adding an additional process to the ones already provided by GitHub "
+"(Issues and Pull Requests) adds more complexity and can be a barrier for "
+"potential first-time contributors."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:133
+msgid ""
+"Expanding the proposal template beyond the single-sentence description "
+"currently required in the features issue template may be a heavy burden "
+"for non-native English speakers."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:137
+msgid "GitHub Issues"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:139
+msgid ""
+"Using GitHub Issues for these kinds of enhancements is doable. One could "
+"use, for example, tags, to differentiate and filter them from other "
+"issues. The main issue is in discussing and reviewing an enhancement: "
+"GitHub issues only have a single thread for comments. Enhancements "
+"usually have multiple threads of discussion at the same time for various "
+"parts of the doc. Managing these multiple discussions can be confusing "
+"when using GitHub Issues."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:141
+msgid "Google Docs"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:143
+msgid ""
+"Google Docs allow for multiple threads of discussions. But as Google Docs"
+" are hosted outside the project, their discoverability by the community "
+"needs to be taken care of. A list of links to all proposals has to be "
+"managed and made available for the community. Compared to shipping "
+"proposals as part of Flower's repository, the potential for missing links"
+" is much higher."
+msgstr ""
+
+#: ../../source/fed/index.md:1
+msgid "FED - Flower Enhancement Doc"
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:2
+msgid "Aggregate evaluation results"
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:4
+msgid ""
+"The Flower server does not prescribe a way to aggregate evaluation "
+"results, but it enables the user to fully customize result aggregation."
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:8
+msgid "Aggregate Custom Evaluation Results"
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:10
+msgid ""
+"The same :code:`Strategy`-customization approach can be used to aggregate"
+" custom evaluation results coming from individual clients. Clients can "
+"return custom metrics to the server by returning a dictionary:"
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:36
+msgid ""
+"The server can then use a customized strategy to aggregate the metrics "
+"provided in these dictionaries:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:2
+msgid "Configure clients"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:4
+msgid ""
+"Along with model parameters, Flower can send configuration values to "
+"clients. Configuration values can be used for various purposes. They are,"
+" for example, a popular way to control client-side hyperparameters from "
+"the server."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:7
+msgid "Configuration values"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:9
+msgid ""
+"Configuration values are represented as a dictionary with ``str`` keys "
+"and values of type ``bool``, ``bytes``, ``double`` (64-bit precision "
+"float), ``int``, or ``str`` (or equivalent types in different languages)."
+" Here is an example of a configuration dictionary in Python:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:20
+msgid ""
+"Flower serializes these configuration dictionaries (or *config dict* for "
+"short) to their ProtoBuf representation, transports them to the client "
+"using gRPC, and then deserializes them back to Python dictionaries."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:24
+msgid ""
+"Currently, there is no support for directly sending collection types "
+"(e.g., ``Set``, ``List``, ``Map``) as values in configuration "
+"dictionaries. There are several workarounds to send collections as values"
+" by converting them to one of the supported value types (and converting "
+"them back on the client-side)."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:26
+msgid ""
+"One can, for example, convert a list of floating-point numbers to a JSON "
+"string, then send the JSON string using the configuration dictionary, and"
+" then convert the JSON string back to a list of floating-point numbers on"
+" the client."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:30
+msgid "Configuration through built-in strategies"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:32
+msgid ""
+"The easiest way to send configuration values to clients is to use a "
+"built-in strategy like :code:`FedAvg`. Built-in strategies support so-"
+"called configuration functions. A configuration function is a function "
+"that the built-in strategy calls to get the configuration dictionary for "
+"the current round. It then forwards the configuration dictionary to all "
+"the clients selected during that round."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:34
+msgid ""
+"Let's start with a simple example. Imagine we want to send (a) the batch "
+"size that the client should use, (b) the current global round of "
+"federated learning, and (c) the number of epochs to train on the client-"
+"side. Our configuration function could look like this:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:47
+msgid ""
+"To make the built-in strategies use this function, we can pass it to "
+"``FedAvg`` during initialization using the parameter "
+":code:`on_fit_config_fn`:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:56
+msgid "One the client side, we receive the configuration dictionary in ``fit``:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:67
+msgid ""
+"There is also an `on_evaluate_config_fn` to configure evaluation, which "
+"works the same way. They are separate functions because one might want to"
+" send different configuration values to `evaluate` (for example, to use a"
+" different batch size)."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:69
+msgid ""
+"The built-in strategies call this function every round (that is, every "
+"time `Strategy.configure_fit` or `Strategy.configure_evaluate` runs). "
+"Calling `on_evaluate_config_fn` every round allows us to vary/change the "
+"config dict over consecutive rounds. If we wanted to implement a "
+"hyperparameter schedule, for example, to increase the number of local "
+"epochs during later rounds, we could do the following:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:82
+msgid "The :code:`FedAvg` strategy will call this function *every round*."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:85
+msgid "Configuring individual clients"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:87
+msgid ""
+"In some cases, it is necessary to send different configuration values to "
+"different clients."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:89
+msgid ""
+"This can be achieved by customizing an existing strategy or by "
+"`implementing a custom strategy from scratch "
+"<https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. "
+"Here's a nonsensical example that customizes :code:`FedAvg` by adding a "
+"custom ``\"hello\": \"world\"`` configuration key/value pair to the "
+"config dict of a *single client* (only the first client in the list, the "
+"other clients in this round to not receive this \"special\" config "
+"value):"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:2
+msgid "Configure logging"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:4
+msgid ""
+"The Flower logger keeps track of all core events that take place in "
+"federated learning workloads. It presents information by default "
+"following a standard message format:"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:13
+msgid ""
+"containing relevant information including: log message level (e.g. "
+":code:`INFO`, :code:`DEBUG`), a timestamp, the line where the logging "
+"took place from, as well as the log message itself. In this way, the "
+"logger would typically display information on your terminal as follows:"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:34
+msgid "Saving log to file"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:36
+msgid ""
+"By default, the Flower log is outputted to the terminal where you launch "
+"your Federated Learning workload from. This applies for both gRPC-based "
+"federation (i.e. when you do :code:`fl.server.start_server`) and when "
+"using the :code:`VirtualClientEngine` (i.e. when you do "
+":code:`fl.simulation.start_simulation`). In some situations you might "
+"want to save this log to disk. You can do so by calling the "
+"`fl.common.logger.configure() "
+"<https://github.com/adap/flower/blob/main/src/py/flwr/common/logger.py>`_"
+" function. For example:"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:53
+msgid ""
+"With the above, Flower will record the log you see on your terminal to "
+":code:`log.txt`. This file will be created in the same directory as were "
+"you are running the code from. If we inspect we see the log above is also"
+" recorded but prefixing with :code:`identifier` each line:"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:74
+msgid "Log your own messages"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:76
+msgid ""
+"You might expand the information shown by default with the Flower logger "
+"by adding more messages relevant to your application. You can achieve "
+"this easily as follows."
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:102
+msgid ""
+"In this way your logger will show, in addition to the default messages, "
+"the ones introduced by the clients as specified above."
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:128
+msgid "Log to a remote service"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:130
+msgid ""
+"The :code:`fl.common.logger.configure` function, also allows specifying a"
+" host to which logs can be pushed (via :code:`POST`) through a native "
+"Python :code:`logging.handler.HTTPHandler`. This is a particularly useful"
+" feature in :code:`gRPC`-based Federated Learning workloads where "
+"otherwise gathering logs from all entities (i.e. the server and the "
+"clients) might be cumbersome. Note that in Flower simulation, the server "
+"automatically displays all logs. You can still specify a "
+":code:`HTTPHandler` should you whish to backup or analyze the logs "
+"somewhere else."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:2
+msgid "Enable SSL connections"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:4
+msgid ""
+"This guide describes how to a SSL-enabled secure Flower server can be "
+"started and how a Flower client can establish a secure connections to it."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:7
+msgid ""
+"A complete code example demonstrating a secure connection can be found "
+"`here <https://github.com/adap/flower/tree/main/examples/advanced-"
+"tensorflow>`_."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:10
+msgid ""
+"The code example comes with a README.md file which will explain how to "
+"start it. Although it is already SSL-enabled, it might be less "
+"descriptive on how. Stick to this guide for a deeper introduction to the "
+"topic."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:16
+msgid "Certificates"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:18
+msgid ""
+"Using SSL-enabled connections requires certificates to be passed to the "
+"server and client. For the purpose of this guide we are going to generate"
+" self-signed certificates. As this can become quite complex we are going "
+"to ask you to run the script in :code:`examples/advanced-"
+"tensorflow/certificates/generate.sh`"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:23
+msgid "with the following command sequence:"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:30
+msgid ""
+"This will generate the certificates in :code:`examples/advanced-"
+"tensorflow/.cache/certificates`."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:32
+msgid ""
+"The approach how the SSL certificates are generated in this example can "
+"serve as an inspiration and starting point but should not be taken as "
+"complete for production environments. Please refer to other sources "
+"regarding the issue of correctly generating certificates for production "
+"environments."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:36
+msgid ""
+"In case you are a researcher you might be just fine using the self-signed"
+" certificates generated using the scripts which are part of this guide."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:41
+msgid "Server"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:43
+msgid ""
+"We are now going to show how to write a sever which uses the previously "
+"generated scripts."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:61
+msgid ""
+"When providing certificates, the server expects a tuple of three "
+"certificates. :code:`Path` can be used to easily read the contents of "
+"those files into byte strings, which is the data type "
+":code:`start_server` expects."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:65
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:37
+#: ../../source/ref-api-flwr.rst:15
+msgid "Client"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:67
+msgid ""
+"We are now going to show how to write a client which uses the previously "
+"generated scripts:"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:84
+msgid ""
+"When setting :code:`root_certificates`, the client expects the PEM-"
+"encoded root certificates as a byte string. We are again using "
+":code:`Path` to simplify reading those as byte strings."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:89
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:287
+msgid "Conclusion"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:91
+msgid ""
+"You should now have learned how to generate self-signed certificates "
+"using the given script, start a SSL-enabled server, and have a client "
+"establish a secure connection to it."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:96
+msgid "Additional resources"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:98
+msgid ""
+"These additional sources might be relevant if you would like to dive "
+"deeper into the topic of certificates:"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:100
+msgid "`Let's Encrypt <https://letsencrypt.org/docs/>`_"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:101
+msgid "`certbot <https://certbot.eff.org/>`_"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:2
+msgid "Implement strategies"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:4
+msgid ""
+"The strategy abstraction enables implementation of fully custom "
+"strategies. A strategy is basically the federated learning algorithm that"
+" runs on the server. Strategies decide how to sample clients, how to "
+"configure clients for training, how to aggregate updates, and how to "
+"evaluate models. Flower provides a few built-in strategies which are "
+"based on the same API described below."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:11
+msgid "The :code:`Strategy` abstraction"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:13
+msgid ""
+"All strategy implementation are derived from the abstract base class "
+":code:`flwr.server.strategy.Strategy`, both built-in implementations and "
+"third party implementations. This means that custom strategy "
+"implementations have the exact same capabilities at their disposal as "
+"built-in ones."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:18
+msgid ""
+"The strategy abstraction defines a few abstract methods that need to be "
+"implemented:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:75
+msgid ""
+"Creating a new strategy means implementing a new :code:`class` (derived "
+"from the abstract base class :code:`Strategy`) that implements for the "
+"previously shown abstract methods:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:100
+msgid "The Flower server calls these methods in the following order:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:177
+msgid "The following sections describe each of those methods in more detail."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:180
+msgid "The :code:`initialize_parameters` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:182
+msgid ""
+":code:`initialize_parameters` is called only once, at the very beginning "
+"of an execution. It is responsible for providing the initial global model"
+" parameters in a serialized form (i.e., as a :code:`Parameters` object)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:184
+msgid ""
+"Built-in strategies return user-provided initial parameters. The "
+"following example shows how initial parameters can be passed to "
+":code:`FedAvg`:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:209
+msgid ""
+"The Flower server will call :code:`initialize_parameters`, which either "
+"returns the parameters that were passed to :code:`initial_parameters`, or"
+" :code:`None`. If no parameters are returned from "
+":code:`initialize_parameters` (i.e., :code:`None`), the server will "
+"randomly select one client and ask it to provide its parameters. This is "
+"a convenience feature and not recommended in practice, but it can be "
+"useful for prototyping. In practice, it is recommended to always use "
+"server-side parameter initialization."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:213
+msgid ""
+"Server-side parameter initialization is a powerful mechanism. It can be "
+"used, for example, to resume training from a previously saved checkpoint."
+" It is also the fundamental capability needed to implement hybrid "
+"approaches, for example, to fine-tune a pre-trained model using federated"
+" learning."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:216
+msgid "The :code:`configure_fit` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:218
+msgid ""
+":code:`configure_fit` is responsible for configuring the upcoming round "
+"of training. What does *configure* mean in this context? Configuring a "
+"round means selecting clients and deciding what instructions to send to "
+"these clients. The signature of :code:`configure_fit` makes this clear:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:231
+msgid ""
+"The return value is a list of tuples, each representing the instructions "
+"that will be sent to a particular client. Strategy implementations "
+"usually perform the following steps in :code:`configure_fit`:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:233
+#: ../../source/how-to-implement-strategies.rst:280
+msgid ""
+"Use the :code:`client_manager` to randomly sample all (or a subset of) "
+"available clients (each represented as a :code:`ClientProxy` object)"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:234
+msgid ""
+"Pair each :code:`ClientProxy` with the same :code:`FitIns` holding the "
+"current global model :code:`parameters` and :code:`config` dict"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:236
+msgid ""
+"More sophisticated implementations can use :code:`configure_fit` to "
+"implement custom client selection logic. A client will only participate "
+"in a round if the corresponding :code:`ClientProxy` is included in the "
+"the list returned from :code:`configure_fit`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:240
+msgid ""
+"The structure of this return value provides a lot of flexibility to the "
+"user. Since instructions are defined on a per-client basis, different "
+"instructions can be sent to each client. This enables custom strategies "
+"to train, for example, different models on different clients, or use "
+"different hyperparameters on different clients (via the :code:`config` "
+"dict)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:243
+msgid "The :code:`aggregate_fit` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:245
+msgid ""
+":code:`aggregate_fit` is responsible for aggregating the results returned"
+" by the clients that were selected and asked to train in "
+":code:`configure_fit`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:258
+msgid ""
+"Of course, failures can happen, so there is no guarantee that the server "
+"will get results from all the clients it sent instructions to (via "
+":code:`configure_fit`). :code:`aggregate_fit` therefore receives a list "
+"of :code:`results`, but also a list of :code:`failures`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:260
+msgid ""
+":code:`aggregate_fit` returns an optional :code:`Parameters` object and a"
+" dictionary of aggregated metrics. The :code:`Parameters` return value is"
+" optional because :code:`aggregate_fit` might decide that the results "
+"provided are not sufficient for aggregation (e.g., too many failures)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:263
+msgid "The :code:`configure_evaluate` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:265
+msgid ""
+":code:`configure_evaluate` is responsible for configuring the upcoming "
+"round of evaluation. What does *configure* mean in this context? "
+"Configuring a round means selecting clients and deciding what "
+"instructions to send to these clients. The signature of "
+":code:`configure_evaluate` makes this clear:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:278
+msgid ""
+"The return value is a list of tuples, each representing the instructions "
+"that will be sent to a particular client. Strategy implementations "
+"usually perform the following steps in :code:`configure_evaluate`:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:281
+msgid ""
+"Pair each :code:`ClientProxy` with the same :code:`EvaluateIns` holding "
+"the current global model :code:`parameters` and :code:`config` dict"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:283
+msgid ""
+"More sophisticated implementations can use :code:`configure_evaluate` to "
+"implement custom client selection logic. A client will only participate "
+"in a round if the corresponding :code:`ClientProxy` is included in the "
+"the list returned from :code:`configure_evaluate`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:287
+msgid ""
+"The structure of this return value provides a lot of flexibility to the "
+"user. Since instructions are defined on a per-client basis, different "
+"instructions can be sent to each client. This enables custom strategies "
+"to evaluate, for example, different models on different clients, or use "
+"different hyperparameters on different clients (via the :code:`config` "
+"dict)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:291
+msgid "The :code:`aggregate_evaluate` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:293
+msgid ""
+":code:`aggregate_evaluate` is responsible for aggregating the results "
+"returned by the clients that were selected and asked to evaluate in "
+":code:`configure_evaluate`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:306
+msgid ""
+"Of course, failures can happen, so there is no guarantee that the server "
+"will get results from all the clients it sent instructions to (via "
+":code:`configure_evaluate`). :code:`aggregate_evaluate` therefore "
+"receives a list of :code:`results`, but also a list of :code:`failures`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:308
+msgid ""
+":code:`aggregate_evaluate` returns an optional :code:`float` (loss) and a"
+" dictionary of aggregated metrics. The :code:`float` return value is "
+"optional because :code:`aggregate_evaluate` might decide that the results"
+" provided are not sufficient for aggregation (e.g., too many failures)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:311
+msgid "The :code:`evaluate` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:313
+msgid ""
+":code:`evaluate` is responsible for evaluating model parameters on the "
+"server-side. Having :code:`evaluate` in addition to "
+":code:`configure_evaluate`/:code:`aggregate_evaluate` enables strategies "
+"to perform both servers-side and client-side (federated) evaluation."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:323
+msgid ""
+"The return value is again optional because the strategy might not need to"
+" implement server-side evaluation or because the user-defined "
+":code:`evaluate` method might not complete successfully (e.g., it might "
+"fail to load the server-side evaluation data)."
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:2
+msgid "Install Flower"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:6
+msgid "Python version"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:12
+msgid "Install stable release"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:14
+msgid ""
+"Stable releases are available on `PyPI "
+"<https://pypi.org/project/flwr/>`_::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:18
+msgid ""
+"For simulations that use the Virtual Client Engine, ``flwr`` should be "
+"installed with the ``simulation`` extra::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:24
+msgid "Verify installation"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:26
+msgid ""
+"The following command can be used to verfiy if Flower was successfully "
+"installed. If everything worked, it should print the version of Flower to"
+" the command line::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:33
+msgid "Advanced installation options"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:36
+msgid "Install pre-release"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:38
+msgid ""
+"New (possibly unstable) versions of Flower are sometimes available as "
+"pre-release versions (alpha, beta, release candidate) before the stable "
+"release happens::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:42
+msgid ""
+"For simulations that use the Virtual Client Engine, ``flwr`` pre-releases"
+" should be installed with the ``simulation`` extra::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:47
+msgid "Install nightly release"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:49
+msgid ""
+"The latest (potentially unstable) changes in Flower are available as "
+"nightly releases::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:53
+msgid ""
+"For simulations that use the Virtual Client Engine, ``flwr-nightly`` "
+"should be installed with the ``simulation`` extra::"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:2
+msgid "Monitor simulation"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:4
+msgid ""
+"Flower allows you to monitor system resources while running your "
+"simulation. Moreover, the Flower simulation engine is powerful and "
+"enables you to decide how to allocate resources per client manner and "
+"constrain the total usage. Insights from resource consumption can help "
+"you make smarter decisions and speed up the execution time."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:6
+msgid ""
+"The specific instructions assume you are using macOS and have the "
+"`Homebrew <https://brew.sh/>`_ package manager installed."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:10
+msgid "Downloads"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:16
+msgid ""
+"`Prometheus <https://prometheus.io/>`_ is used for data collection, while"
+" `Grafana <https://grafana.com/>`_ will enable you to visualize the "
+"collected data. They are both well integrated with `Ray "
+"<https://www.ray.io/>`_ which Flower uses under the hood."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:18
+msgid ""
+"Overwrite the configuration files (depending on your device, it might be "
+"installed on a different path)."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:20
+msgid "If you are on an M1 Mac, it should be:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:27
+msgid "On the previous generation Intel Mac devices, it should be:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:34
+msgid ""
+"Open the respective configuration files and change them. Depending on "
+"your device, use one of the two following commands:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:44
+msgid ""
+"and then delete all the text in the file and paste a new Prometheus "
+"config you see below. You may adjust the time intervals to your "
+"requirements:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:59
+msgid ""
+"Now after you have edited the Prometheus configuration, do the same with "
+"the Grafana configuration files. Open those using one of the following "
+"commands as before:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:69
+msgid ""
+"Your terminal editor should open and allow you to apply the following "
+"configuration as before."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:84
+msgid ""
+"Congratulations, you just downloaded all the necessary software needed "
+"for metrics tracking. Now, let’s start it."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:88
+msgid "Tracking metrics"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:90
+msgid ""
+"Before running your Flower simulation, you have to start the monitoring "
+"tools you have just installed and configured."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:97
+msgid ""
+"Please include the following argument in your Python code when starting a"
+" simulation."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:108
+msgid "Now, you are ready to start your workload."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:110
+msgid ""
+"Shortly after the simulation starts, you should see the following logs in"
+" your terminal:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:117
+msgid "You can look at everything at `<http://127.0.0.1:8265>`_ ."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:119
+msgid ""
+"It's a Ray Dashboard. You can navigate to Metrics (on the left panel, the"
+" lowest option)."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:121
+msgid ""
+"Or alternatively, you can just see them in Grafana by clicking on the "
+"right-up corner, “View in Grafana”. Please note that the Ray dashboard is"
+" only accessible during the simulation. After the simulation ends, you "
+"can only use Grafana to explore the metrics. You can start Grafana by "
+"going to ``http://localhost:3000/``."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:123
+msgid ""
+"After you finish the visualization, stop Prometheus and Grafana. This is "
+"important as they will otherwise block, for example port :code:`3000` on "
+"your machine as long as they are running."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:132
+msgid "Resource allocation"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:134
+msgid ""
+"You must understand how the Ray library works to efficiently allocate "
+"system resources to simulation clients on your own."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:136
+msgid ""
+"Initially, the simulation (which Ray handles under the hood) starts by "
+"default with all the available resources on the system, which it shares "
+"among the clients. It doesn't mean it divides it equally among all of "
+"them, nor that the model training happens at all of them simultaneously. "
+"You will learn more about that in the later part of this blog. You can "
+"check the system resources by running the following:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:143
+msgid "In Google Colab, the result you see might be similar to this:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:155
+msgid ""
+"However, you can overwrite the defaults. When starting a simulation, do "
+"the following (you don't need to overwrite all of them):"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:175
+msgid "Let’s also specify the resource for a single client."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:205
+msgid ""
+"Now comes the crucial part. Ray will start a new client only when it has "
+"all the required resources (such that they run in parallel) when the "
+"resources allow."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:207
+msgid ""
+"In the example above, only one client will be run, so your clients won't "
+"run concurrently. Setting :code:`client_num_gpus = 0.5` would allow "
+"running two clients and therefore enable them to run concurrently. Be "
+"careful not to require more resources than available. If you specified "
+":code:`client_num_gpus = 2`, the simulation wouldn't start (even if you "
+"had 2 GPUs but decided to set 1 in :code:`ray_init_args`)."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:212 ../../source/ref-faq.rst:2
+msgid "FAQ"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:214
+msgid "Q: I don't see any metrics logged."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:216
+msgid ""
+"A: The timeframe might not be properly set. The setting is in the top "
+"right corner (\"Last 30 minutes\" by default). Please change the "
+"timeframe to reflect the period when the simulation was running."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:218
+msgid ""
+"Q: I see “Grafana server not detected. Please make sure the Grafana "
+"server is running and refresh this page” after going to the Metrics tab "
+"in Ray Dashboard."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:220
+msgid ""
+"A: You probably don't have Grafana running. Please check the running "
+"services"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:226
+msgid ""
+"Q: I see \"This site can't be reached\" when going to "
+"`<http://127.0.0.1:8265>`_."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:228
+msgid ""
+"A: Either the simulation has already finished, or you still need to start"
+" Prometheus."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:232
+msgid "Resources"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:234
+msgid ""
+"Ray Dashboard: `<https://docs.ray.io/en/latest/ray-core/ray-"
+"dashboard.html>`_"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:236
+msgid ""
+"Ray Metrics: `<https://docs.ray.io/en/latest/ray-observability/ray-"
+"metrics.html>`_"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:2
+msgid "Run simulations"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:8
+msgid ""
+"Simulating Federated Learning workloads is useful for a multitude of use-"
+"cases: you might want to run your workload on a large cohort of clients "
+"but without having to source, configure and mange a large number of "
+"physical devices; you might want to run your FL workloads as fast as "
+"possible on the compute systems you have access to without having to go "
+"through a complex setup process; you might want to validate your "
+"algorithm on different scenarios at varying levels of data and system "
+"heterogeneity, client availability, privacy budgets, etc. These are among"
+" some of the use-cases where simulating FL workloads makes sense. Flower "
+"can accommodate these scenarios by means of its `VirtualClientEngine "
+"<contributor-explanation-architecture.html#virtual-client-engine>`_ or "
+"VCE."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:10
+msgid ""
+"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`"
+" clients. These clients are identical to `non-virtual` clients (i.e. the "
+"ones you launch via the command `flwr.client.start_numpy_client <ref-api-"
+"flwr.html#start-numpy-client>`_) in the sense that they can be configure "
+"by creating a class inheriting, for example, from "
+"`flwr.client.NumPyClient <ref-api-flwr.html#flwr.client.NumPyClient>`_ "
+"and therefore behave in an identical way. In addition to that, clients "
+"managed by the :code:`VirtualClientEngine` are:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:12
+msgid ""
+"resource-aware: this means that each client gets assigned a portion of "
+"the compute and memory on your system. You as a user can control this at "
+"the beginning of the simulation and allows you to control the degree of "
+"parallelism of your Flower FL simulation. The fewer the resources per "
+"client, the more clients can run concurrently on the same hardware."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:13
+msgid ""
+"self-managed: this means that you as a user do not need to launch clients"
+" manually, instead this gets delegated to :code:`VirtualClientEngine`'s "
+"internals."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:14
+msgid ""
+"ephemeral: this means that a client is only materialized when it is "
+"required in the FL process (e.g. to do `fit() <ref-api-"
+"flwr.html#flwr.client.Client.fit>`_). The object is destroyed afterwards,"
+" releasing the resources it was assigned and allowing in this way other "
+"clients to participate."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:16
+msgid ""
+"The :code:`VirtualClientEngine` implements `virtual` clients using `Ray "
+"<https://www.ray.io/>`_, an open-source framework for scalable Python "
+"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use "
+"of `Actors <https://docs.ray.io/en/latest/ray-core/actors.html>`_ to "
+"spawn `virtual` clients and run their workload."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:20
+msgid "Launch your Flower simulation"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:22
+msgid ""
+"Running Flower simulations still require you to define your client class,"
+" a strategy, and utility functions to download and load (and potentially "
+"partition) your dataset. With that out of the way, launching your "
+"simulation is done with `start_simulation <ref-api-"
+"flwr.html#flwr.simulation.start_simulation>`_ and a minimal example looks"
+" as follows:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:44
+msgid "VirtualClientEngine resources"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:45
+msgid ""
+"By default the VCE has access to all system resources (i.e. all CPUs, all"
+" GPUs, etc) since that is also the default behavior when starting Ray. "
+"However, in some settings you might want to limit how many of your system"
+" resources are used for simulation. You can do this via the "
+":code:`ray_init_args` input argument to :code:`start_simulation` which "
+"the VCE internally passes to Ray's :code:`ray.init` command. For a "
+"complete list of settings you can configure check the `ray.init "
+"<https://docs.ray.io/en/latest/ray-core/api/doc/ray.init.html#ray-init>`_"
+" documentation. Do not set :code:`ray_init_args` if you want the VCE to "
+"use all your system's CPUs and GPUs."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:62
+msgid "Assigning client resources"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:63
+msgid ""
+"By default the :code:`VirtualClientEngine` assigns a single CPU core (and"
+" nothing else) to each virtual client. This means that if your system has"
+" 10 cores, that many virtual clients can be concurrently running."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:65
+msgid ""
+"More often than not, you would probably like to adjust the resources your"
+" clients get assigned based on the complexity (i.e. compute and memory "
+"footprint) of your FL workload. You can do so when starting your "
+"simulation by setting the argument `client_resources` to "
+"`start_simulation <ref-api-flwr.html#flwr.simulation.start_simulation>`_."
+" Two keys are internally used by Ray to schedule and spawn workloads (in "
+"our case Flower clients):"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:67
+msgid ":code:`num_cpus` indicates the number of CPU cores a client would get."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:68
+msgid ""
+":code:`num_gpus` indicates the **ratio** of GPU memory a client gets "
+"assigned."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:70
+msgid "Let's see a few examples:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:89
+msgid ""
+"While the :code:`client_resources` can be used to control the degree of "
+"concurrency in your FL simulation, this does not stop you from running "
+"dozens, hundreds or even thousands of clients in the same round and "
+"having orders of magnitude more `dormant` (i.e. not participating in a "
+"round) clients. Let's say you want to have 100 clients per round but your"
+" system can only accommodate 8 clients concurrently. The "
+":code:`VirtualClientEngine` will schedule 100 jobs to run (each "
+"simulating a client sampled by the strategy) and then will execute them "
+"in a resource-aware manner in batches of 8."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:91
+msgid ""
+"To understand all the intricate details on how resources are used to "
+"schedule FL clients and how to define custom resources, please take a "
+"look at the `Ray documentation <https://docs.ray.io/en/latest/ray-"
+"core/scheduling/resources.html>`_."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:94
+msgid "Simulation examples"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:96
+msgid ""
+"A few ready-to-run complete examples for Flower simulation in "
+"Tensorflow/Keras and PyTorch are provided in the `Flower repository "
+"<https://github.com/adap/flower>`_. You can run them on Google Colab too:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:98
+msgid ""
+"`Tensorflow/Keras Simulation "
+"<https://github.com/adap/flower/tree/main/examples/simulation-"
+"tensorflow>`_: 100 clients collaboratively train a MLP model on MNIST."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:99
+msgid ""
+"`PyTorch Simulation <https://github.com/adap/flower/tree/main/examples"
+"/simulation-pytorch>`_: 100 clients collaboratively train a CNN model on "
+"MNIST."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:104
+msgid "Multi-node Flower simulations"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:106
+msgid ""
+"Flower's :code:`VirtualClientEngine` allows you to run FL simulations "
+"across multiple compute nodes. Before starting your multi-node simulation"
+" ensure that you:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:108
+msgid "Have the same Python environment in all nodes."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:109
+msgid "Have a copy of your code (e.g. your entire repo) in all nodes."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:110
+msgid ""
+"Have a copy of your dataset in all nodes (more about this in "
+":ref:`simulation considerations <considerations-for-simulations>`)"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:111
+msgid ""
+"Pass :code:`ray_init_args={\"address\"=\"auto\"}` to `start_simulation "
+"<ref-api-flwr.html#flwr.simulation.start_simulation>`_ so the "
+":code:`VirtualClientEngine` attaches to a running Ray instance."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:112
+msgid ""
+"Start Ray on you head node: on the terminal type :code:`ray start "
+"--head`. This command will print a few lines, one of which indicates how "
+"to attach other nodes to the head node."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:113
+msgid ""
+"Attach other nodes to the head node: copy the command shown after "
+"starting the head and execute it on terminal of a new node: for example "
+":code:`ray start --address='192.168.1.132:6379'`"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:115
+msgid ""
+"With all the above done, you can run your code from the head node as you "
+"would if the simulation was running on a single node."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:117
+msgid ""
+"Once your simulation is finished, if you'd like to dismantle your cluster"
+" you simply need to run the command :code:`ray stop` in each node's "
+"terminal (including the head node)."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:120
+msgid "Multi-node simulation good-to-know"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:122
+msgid ""
+"Here we list a few interesting functionality when running multi-node FL "
+"simulations:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:124
+msgid ""
+"User :code:`ray status` to check all nodes connected to your head node as"
+" well as the total resources available to the "
+":code:`VirtualClientEngine`."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:126
+msgid ""
+"When attaching a new node to the head, all its resources (i.e. all CPUs, "
+"all GPUs) will be visible by the head node. This means that the "
+":code:`VirtualClientEngine` can schedule as many `virtual` clients as "
+"that node can possible run. In some settings you might want to exclude "
+"certain resources from the simulation. You can do this by appending "
+"`--num-cpus=<NUM_CPUS_FROM_NODE>` and/or `--num-"
+"gpus=<NUM_GPUS_FROM_NODE>` in any :code:`ray start` command (including "
+"when starting the head)"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:132
+msgid "Considerations for simulations"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:135
+msgid ""
+"We are actively working on these fronts so to make it trivial to run any "
+"FL workload with Flower simulation."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:138
+msgid ""
+"The current VCE allows you to run Federated Learning workloads in "
+"simulation mode whether you are prototyping simple scenarios on your "
+"personal laptop or you want to train a complex FL pipeline across "
+"multiple high-performance GPU nodes. While we add more capabilities to "
+"the VCE, the points below highlight some of the considerations to keep in"
+" mind when designing your FL pipeline with Flower. We also highlight a "
+"couple of current limitations in our implementation."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:141
+msgid "GPU resources"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:143
+msgid ""
+"The VCE assigns a share of GPU memory to a client that specifies the key "
+":code:`num_gpus` in :code:`client_resources`. This being said, Ray (used "
+"internally by the VCE) is by default:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:146
+msgid ""
+"not aware of the total VRAM available on the GPUs. This means that if you"
+" set :code:`num_gpus=0.5` and you have two GPUs in your system with "
+"different (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients"
+" concurrently."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:147
+msgid ""
+"not aware of other unrelated (i.e. not created by the VCE) workloads are "
+"running on the GPU. Two takeaways from this are:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:149
+msgid ""
+"Your Flower server might need a GPU to evaluate the `global model` after "
+"aggregation (by instance when making use of the `evaluate method <how-to-"
+"implement-strategies.html#the-evaluate-method>`_)"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:150
+msgid ""
+"If you want to run several independent Flower simulations on the same "
+"machine you need to mask-out your GPUs with "
+":code:`CUDA_VISIBLE_DEVICES=\"<GPU_IDs>\"` when launching your "
+"experiment."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:153
+msgid ""
+"In addition, the GPU resource limits passed to :code:`client_resources` "
+"are not `enforced` (i.e. they can be exceeded) which can result in the "
+"situation of client using more VRAM than the ratio specified when "
+"starting the simulation."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:156
+msgid "TensorFlow with GPUs"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:158
+msgid ""
+"When `using a GPU with TensorFlow "
+"<https://www.tensorflow.org/guide/gpu>`_ nearly your entire GPU memory of"
+" all your GPUs visible to the process will be mapped. This is done by "
+"TensorFlow for optimization purposes. However, in settings such as FL "
+"simulations where we want to split the GPU into multiple `virtual` "
+"clients, this is not a desirable mechanism. Luckily we can disable this "
+"default behavior by `enabling memory growth "
+"<https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth>`_."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:160
+msgid ""
+"This would need to be done in the main process (which is where the server"
+" would run) and in each Actor created by the VCE. By means of "
+":code:`actor_kwargs` we can pass the reserved key `\"on_actor_init_fn\"` "
+"in order to specify a function to be executed upon actor initialization. "
+"In this case, to enable GPU growth for TF workloads. It would look as "
+"follows:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:179
+msgid ""
+"This is precisely the mechanism used in `Tensorflow/Keras Simulation "
+"<https://github.com/adap/flower/tree/main/examples/simulation-"
+"tensorflow>`_ example."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:183
+msgid "Multi-node setups"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:185
+msgid ""
+"The VCE does not currently offer a way to control on which node a "
+"particular `virtual` client is executed. In other words, if more than a "
+"single node have the resources needed by a client to run, then any of "
+"those nodes could get the client workload scheduled onto. Later in the FL"
+" process (i.e. in a different round) the same client could be executed by"
+" a different node. Depending on how your clients access their datasets, "
+"this might require either having a copy of all dataset partitions on all "
+"nodes or a dataset serving mechanism (e.g. using nfs, a database) to "
+"circumvent data duplication."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:187
+msgid ""
+"By definition virtual clients are `stateless` due to their ephemeral "
+"nature. A client state can be implemented as part of the Flower client "
+"class but users need to ensure this saved to persistent storage (e.g. a "
+"database, disk) and that can be retrieve later by the same client "
+"regardless on which node it is running from. This is related to the point"
+" above also since, in some way, the client's dataset could be seen as a "
+"type of `state`."
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:2
+msgid "Save and load model checkpoints"
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:4
+msgid ""
+"Flower does not automatically save model updates on the server-side. This"
+" how-to guide describes the steps to save (and load) model checkpoints in"
+" Flower."
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:8
+msgid "Model checkpointing"
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:10
+msgid ""
+"Model updates can be persisted on the server-side by customizing "
+":code:`Strategy` methods. Implementing custom strategies is always an "
+"option, but for many cases it may be more convenient to simply customize "
+"an existing strategy. The following code example defines a new "
+":code:`SaveModelStrategy` which customized the existing built-in "
+":code:`FedAvg` strategy. In particular, it customizes "
+":code:`aggregate_fit` by calling :code:`aggregate_fit` in the base class "
+"(:code:`FedAvg`). It then continues to save returned (aggregated) weights"
+" before it returns those aggregated weights to the caller (i.e., the "
+"server):"
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:47
+msgid "Save and load PyTorch checkpoints"
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:49
+msgid ""
+"Similar to the previous example but with a few extra steps, we'll show "
+"how to store a PyTorch checkpoint we'll use the ``torch.save`` function. "
+"Firstly, ``aggregate_fit`` returns a ``Parameters`` object that has to be"
+" transformed into a list of NumPy ``ndarray``'s, then those are "
+"transformed into the PyTorch ``state_dict`` following the ``OrderedDict``"
+" class structure."
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:85
+msgid ""
+"To load your progress, you simply append the following lines to your "
+"code. Note that this will iterate over all saved checkpoints and load the"
+" latest one:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:2
+msgid "Upgrade to Flower 1.0"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:4
+msgid ""
+"Flower 1.0 is here. Along with new features, Flower 1.0 provides a stable"
+" foundation for future growth. Compared to Flower 0.19 (and other 0.x "
+"series releases), there are a few breaking changes that make it necessary"
+" to change the code of existing 0.x-series projects."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:8
+msgid "Install update"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:10
+msgid ""
+"Here's how to update an existing installation to Flower 1.0 using either "
+"pip or Poetry:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:12
+msgid "pip: add ``-U`` when installing."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:14
+msgid ""
+"``python -m pip install -U flwr`` (when using ``start_server`` and "
+"``start_client``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:15
+msgid ""
+"``python -m pip install -U flwr[simulation]`` (when using "
+"``start_simulation``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:17
+msgid ""
+"Poetry: update the ``flwr`` dependency in ``pyproject.toml`` and then "
+"reinstall (don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` "
+"before running ``poetry install``)."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:19
+msgid "``flwr = \"^1.0.0\"`` (when using ``start_server`` and ``start_client``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:20
+msgid ""
+"``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (when "
+"using ``start_simulation``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:24
+msgid "Required changes"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:26
+msgid "The following breaking changes require manual updates."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:29
+msgid "General"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:31
+msgid ""
+"Pass all arguments as keyword arguments (not as positional arguments). "
+"Here's an example:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:33
+msgid ""
+"Flower 0.19 (positional arguments): ``start_client(\"127.0.0.1:8080\", "
+"FlowerClient())``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:34
+msgid ""
+"Flower 1.0 (keyword arguments): "
+"``start_client(server_address=\"127.0.0.1:8080\", "
+"client=FlowerClient())``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:39
+msgid ""
+"Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to "
+"``def get_parameters(self, config):``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:40
+msgid ""
+"Subclasses of ``Client``: change ``def get_parameters(self):``` to ``def "
+"get_parameters(self, ins: GetParametersIns):``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:43
+msgid "Strategies / ``start_server`` / ``start_simulation``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:45
+msgid ""
+"Pass ``ServerConfig`` (instead of a dictionary) to ``start_server`` and "
+"``start_simulation``. Here's an example:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:47
+msgid ""
+"Flower 0.19: ``start_server(..., config={\"num_rounds\": 3, "
+"\"round_timeout\": 600.0}, ...)``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:48
+msgid ""
+"Flower 1.0: ``start_server(..., "
+"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), "
+"...)``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:50
+msgid ""
+"Replace ``num_rounds=1`` in ``start_simulation`` with the new "
+"``config=ServerConfig(...)`` (see previous item)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:51
+msgid ""
+"Remove ``force_final_distributed_eval`` parameter from calls to "
+"``start_server``. Distributed evaluation on all clients can be enabled by"
+" configuring the strategy to sample all clients for evaluation after the "
+"last round of training."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:52
+msgid "Rename parameter/ndarray conversion functions:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:54
+msgid "``parameters_to_weights`` --> ``parameters_to_ndarrays``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:55
+msgid "``weights_to_parameters`` --> ``ndarrays_to_parameters``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:57
+msgid ""
+"Strategy initialization: if the strategy relies on the default values for"
+" ``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and "
+"``fraction_evaluate`` manually to ``0.1``. Projects that do not manually "
+"create a strategy (by calling ``start_server`` or ``start_simulation`` "
+"without passing a strategy instance) should now manually initialize "
+"FedAvg with ``fraction_fit`` and ``fraction_evaluate`` set to ``0.1``."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:58
+msgid "Rename built-in strategy parameters (e.g., ``FedAvg``):"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:60
+msgid "``fraction_eval`` --> ``fraction_evaluate``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:61
+msgid "``min_eval_clients`` --> ``min_evaluate_clients``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:62
+msgid "``eval_fn`` --> ``evaluate_fn``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:64
+msgid ""
+"Rename ``rnd`` to ``server_round``. This impacts multiple methods and "
+"functions, for example, ``configure_fit``, ``aggregate_fit``, "
+"``configure_evaluate``, ``aggregate_evaluate``, and ``evaluate_fn``."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:65
+msgid "Add ``server_round`` and ``config`` to ``evaluate_fn``:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:67
+msgid ""
+"Flower 0.19: ``def evaluate(parameters: NDArrays) -> "
+"Optional[Tuple[float, Dict[str, Scalar]]]:``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:68
+msgid ""
+"Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, "
+"config: Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, "
+"Scalar]]]:``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:71
+msgid "Custom strategies"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:73
+msgid ""
+"The type of parameter ``failures`` has changed from "
+"``List[BaseException]`` to ``List[Union[Tuple[ClientProxy, FitRes], "
+"BaseException]]`` (in ``aggregate_fit``) and "
+"``List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]]`` (in "
+"``aggregate_evaluate``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:74
+msgid ""
+"The ``Strategy`` method ``evaluate`` now receives the current round of "
+"federated learning/evaluation as the first parameter:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:76
+msgid ""
+"Flower 0.19: ``def evaluate(self, parameters: Parameters) -> "
+"Optional[Tuple[float, Dict[str, Scalar]]]:``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:77
+msgid ""
+"Flower 1.0: ``def evaluate(self, server_round: int, parameters: "
+"Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:80
+msgid "Optional improvements"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:82
+msgid ""
+"Along with the necessary changes above, there are a number of potential "
+"improvements that just became possible:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:84
+msgid ""
+"Remove \"placeholder\" methods from subclasses of ``Client`` or "
+"``NumPyClient``. If you, for example, use server-side evaluation, then "
+"empy placeholder implementations of ``evaluate`` are no longer necessary."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:85
+msgid ""
+"Configure the round timeout via ``start_simulation``: "
+"``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, "
+"round_timeout=600.0), ...)``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:89
+msgid "Further help"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:91
+msgid ""
+"Most official `Flower code examples "
+"<https://github.com/adap/flower/tree/main/examples>`_ are already updated"
+" to Flower 1.0, they can serve as a reference for using the Flower 1.0 "
+"API. If there are further questionsm, `join the Flower Slack "
+"<https://flower.dev/join-slack/>`_ and use the channgel ``#questions``."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:2
+msgid "Use strategies"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:4
+msgid ""
+"Flower allows full customization of the learning process through the "
+":code:`Strategy` abstraction. A number of built-in strategies are "
+"provided in the core framework."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:6
+msgid ""
+"There are three ways to customize the way Flower orchestrates the "
+"learning process on the server side:"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:8
+msgid "Use an existing strategy, for example, :code:`FedAvg`"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:9
+#: ../../source/how-to-use-strategies.rst:40
+msgid "Customize an existing strategy with callback functions"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:10
+#: ../../source/how-to-use-strategies.rst:87
+msgid "Implement a novel strategy"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:14
+msgid "Use an existing strategy"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:16
+msgid ""
+"Flower comes with a number of popular federated learning strategies "
+"built-in. A built-in strategy can be instantiated as follows:"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:25
+msgid ""
+"This creates a strategy with all parameters left at their default values "
+"and passes it to the :code:`start_server` function. It is usually "
+"recommended to adjust a few parameters during instantiation:"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:42
+msgid ""
+"Existing strategies provide several ways to customize their behaviour. "
+"Callback functions allow strategies to call user-provided code during "
+"execution."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:45
+msgid "Configuring client fit and client evaluate"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:47
+msgid ""
+"The server can pass new configuration values to the client each round by "
+"providing a function to :code:`on_fit_config_fn`. The provided function "
+"will be called by the strategy and must return a dictionary of "
+"configuration key values pairs that will be sent to the client. It must "
+"return a dictionary of arbitraty configuration values  :code:`client.fit`"
+" and :code:`client.evaluate` functions during each round of federated "
+"learning."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:75
+msgid ""
+"The :code:`on_fit_config_fn` can be used to pass arbitrary configuration "
+"values from server to client, and poetentially change these values each "
+"round, for example, to adjust the learning rate. The client will receive "
+"the dictionary returned by the :code:`on_fit_config_fn` in its own "
+":code:`client.fit()` function."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:78
+msgid ""
+"Similar to :code:`on_fit_config_fn`, there is also "
+":code:`on_evaluate_config_fn` to customize the configuration sent to "
+":code:`client.evaluate()`"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:81
+msgid "Configuring server-side evaluation"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:83
+msgid ""
+"Server-side evaluation can be enabled by passing an evaluation function "
+"to :code:`evaluate_fn`."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:89
+msgid ""
+"Writing a fully custom strategy is a bit more involved, but it provides "
+"the most flexibility. Read the `Implementing Strategies <how-to-"
+"implement-strategies.html>`_ guide to learn more."
+msgstr ""
+
+#: ../../source/index.rst:34
+msgid "Tutorial"
+msgstr ""
+
+#: ../../source/index.rst:44
+msgid "Quickstart tutorials"
+msgstr ""
+
+#: ../../source/index.rst:75 ../../source/index.rst:79
+msgid "How-to guides"
+msgstr ""
+
+#: ../../source/index.rst:95
+msgid "Legacy example guides"
+msgstr ""
+
+#: ../../source/index.rst:106 ../../source/index.rst:110
+msgid "Explanations"
+msgstr ""
+
+#: ../../source/index.rst:122
+msgid "API reference"
+msgstr ""
+
+#: ../../source/index.rst:129
+msgid "Reference docs"
+msgstr ""
+
+#: ../../source/index.rst:145
+msgid "Contributor tutorials"
+msgstr ""
+
+#: ../../source/index.rst:152
+msgid "Contributor how-to guides"
+msgstr ""
+
+#: ../../source/index.rst:164
+msgid "Contributor explanations"
+msgstr ""
+
+#: ../../source/index.rst:170
+msgid "Contributor references"
+msgstr ""
+
+#: ../../source/index.rst:-1
+msgid ""
+"Check out the documentation of the main Flower Framework enabling easy "
+"Python development for Federated Learning."
+msgstr ""
+
+#: ../../source/index.rst:2
+msgid "Flower Framework Documentation"
+msgstr ""
+
+#: ../../source/index.rst:7
+msgid ""
+"Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a "
+"friendly federated learning framework."
+msgstr ""
+
+#: ../../source/index.rst:11
+msgid "Join the Flower Community"
+msgstr ""
+
+#: ../../source/index.rst:13
+msgid ""
+"The Flower Community is growing quickly - we're a friendly group of "
+"researchers, engineers, students, professionals, academics, and other "
+"enthusiasts."
+msgstr ""
+
+#: ../../source/index.rst:15
+msgid "Join us on Slack"
+msgstr ""
+
+#: ../../source/index.rst:23
+msgid "Flower Framework"
+msgstr ""
+
+#: ../../source/index.rst:25
+msgid ""
+"The user guide is targeted at researchers and developers who want to use "
+"Flower to bring existing machine learning workloads into a federated "
+"setting. One of Flower's design goals was to make this simple. Read on to"
+" learn more."
+msgstr ""
+
+#: ../../source/index.rst:30
+msgid "Tutorials"
+msgstr ""
+
+#: ../../source/index.rst:32
+msgid ""
+"A learning-oriented series of federated learning tutorials, the best "
+"place to start."
+msgstr ""
+
+#: ../../source/index.rst:62
+msgid ""
+"QUICKSTART TUTORIALS: :doc:`PyTorch <tutorial-quickstart-pytorch>` | "
+":doc:`TensorFlow <tutorial-quickstart-tensorflow>` | :doc:`🤗 Transformers"
+" <tutorial-quickstart-huggingface>` | :doc:`JAX <tutorial-quickstart-"
+"jax>` | :doc:`Pandas <tutorial-quickstart-pandas>` | :doc:`fastai "
+"<tutorial-quickstart-fastai>` | :doc:`PyTorch Lightning <tutorial-"
+"quickstart-pytorch-lightning>` | :doc:`MXNet <tutorial-quickstart-mxnet>`"
+" | :doc:`scikit-learn <tutorial-quickstart-scikitlearn>` | :doc:`XGBoost "
+"<tutorial-quickstart-xgboost>` | :doc:`Android <tutorial-quickstart-"
+"android>` | :doc:`iOS <tutorial-quickstart-ios>`"
+msgstr ""
+
+#: ../../source/index.rst:64
+msgid "We also made video tutorials for PyTorch:"
+msgstr ""
+
+#: ../../source/index.rst:69
+msgid "And TensorFlow:"
+msgstr ""
+
+#: ../../source/index.rst:77
+msgid ""
+"Problem-oriented how-to guides show step-by-step how to achieve a "
+"specific goal."
+msgstr ""
+
+#: ../../source/index.rst:108
+msgid ""
+"Understanding-oriented concept guides explain and discuss key topics and "
+"underlying ideas behind Flower and collaborative AI."
+msgstr ""
+
+#: ../../source/index.rst:118
+msgid "References"
+msgstr ""
+
+#: ../../source/index.rst:120
+msgid "Information-oriented API reference and other reference material."
+msgstr ""
+
+#: ../../source/index.rst:140
+msgid "Contributor docs"
+msgstr ""
+
+#: ../../source/index.rst:142
+msgid ""
+"The Flower community welcomes contributions. The following docs are "
+"intended to help along the way."
+msgstr ""
+
+#: ../../source/ref-api-cli.rst:2
+msgid "Flower CLI reference"
+msgstr ""
+
+#: ../../source/ref-api-cli.rst:7
+msgid "flower-server"
+msgstr ""
+
+#: ../../source/ref-api-cli.rst:17
+msgid "flower-driver-api"
+msgstr ""
+
+#: ../../source/ref-api-cli.rst:27
+msgid "flower-fleet-api"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:2
+msgid "flwr (Python API reference)"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:8
+msgid "client"
+msgstr ""
+
+#: flwr.client:1 of
+msgid "Flower client."
+msgstr ""
+
+#: flwr.client.client.Client:1 of
+msgid "Abstract base class for Flower clients."
+msgstr ""
+
+#: flwr.client.client.Client.evaluate:1
+#: flwr.client.numpy_client.NumPyClient.evaluate:1 of
+msgid "Evaluate the provided parameters using the locally held dataset."
+msgstr ""
+
+#: flwr.client.app.start_client flwr.client.app.start_numpy_client
+#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit
+#: flwr.client.client.Client.get_parameters
+#: flwr.client.client.Client.get_properties
+#: flwr.client.numpy_client.NumPyClient.evaluate
+#: flwr.client.numpy_client.NumPyClient.fit
+#: flwr.client.numpy_client.NumPyClient.get_parameters
+#: flwr.client.numpy_client.NumPyClient.get_properties
+#: flwr.server.app.start_server flwr.server.strategy.bulyan.Bulyan.__init__
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__
+#: flwr.server.strategy.fedadam.FedAdam.__init__
+#: flwr.server.strategy.fedavg.FedAvg.__init__
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__
+#: flwr.server.strategy.fedopt.FedOpt.__init__
+#: flwr.server.strategy.fedprox.FedProx.__init__
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__
+#: flwr.server.strategy.fedyogi.FedYogi.__init__
+#: flwr.server.strategy.krum.Krum.__init__
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate
+#: flwr.server.strategy.strategy.Strategy.configure_fit
+#: flwr.server.strategy.strategy.Strategy.evaluate
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters
+#: flwr.simulation.app.start_simulation of
+msgid "Parameters"
+msgstr ""
+
+#: flwr.client.client.Client.evaluate:3 of
+msgid ""
+"The evaluation instructions containing (global) model parameters received"
+" from the server and a dictionary of configuration values used to "
+"customize the local evaluation process."
+msgstr ""
+
+#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit
+#: flwr.client.client.Client.get_parameters
+#: flwr.client.client.Client.get_properties
+#: flwr.client.numpy_client.NumPyClient.evaluate
+#: flwr.client.numpy_client.NumPyClient.fit
+#: flwr.client.numpy_client.NumPyClient.get_parameters
+#: flwr.client.numpy_client.NumPyClient.get_properties
+#: flwr.server.app.start_server
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate
+#: flwr.server.strategy.strategy.Strategy.configure_fit
+#: flwr.server.strategy.strategy.Strategy.evaluate
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters
+#: flwr.simulation.app.start_simulation of
+msgid "Returns"
+msgstr ""
+
+#: flwr.client.client.Client.evaluate:8 of
+msgid ""
+"The evaluation result containing the loss on the local dataset and other "
+"details such as the number of local data examples used for evaluation."
+msgstr ""
+
+#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit
+#: flwr.client.client.Client.get_parameters
+#: flwr.client.client.Client.get_properties
+#: flwr.client.numpy_client.NumPyClient.get_parameters
+#: flwr.client.numpy_client.NumPyClient.get_properties
+#: flwr.server.app.start_server
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate
+#: flwr.server.strategy.strategy.Strategy.configure_fit
+#: flwr.server.strategy.strategy.Strategy.evaluate
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters
+#: flwr.simulation.app.start_simulation of
+msgid "Return type"
+msgstr ""
+
+#: flwr.client.client.Client.fit:1 of
+msgid "Refine the provided parameters using the locally held dataset."
+msgstr ""
+
+#: flwr.client.client.Client.fit:3 of
+msgid ""
+"The training instructions containing (global) model parameters received "
+"from the server and a dictionary of configuration values used to "
+"customize the local training process."
+msgstr ""
+
+#: flwr.client.client.Client.fit:8 of
+msgid ""
+"The training result containing updated parameters and other details such "
+"as the number of local training examples used for training."
+msgstr ""
+
+#: flwr.client.client.Client.get_parameters:1
+#: flwr.client.numpy_client.NumPyClient.get_parameters:1 of
+msgid "Return the current local model parameters."
+msgstr ""
+
+#: flwr.client.client.Client.get_parameters:3 of
+msgid ""
+"The get parameters instructions received from the server containing a "
+"dictionary of configuration values."
+msgstr ""
+
+#: flwr.client.client.Client.get_parameters:7 of
+msgid "The current local model parameters."
+msgstr ""
+
+#: flwr.client.client.Client.get_properties:1 of
+msgid "Return set of client's properties."
+msgstr ""
+
+#: flwr.client.client.Client.get_properties:3 of
+msgid ""
+"The get properties instructions received from the server containing a "
+"dictionary of configuration values."
+msgstr ""
+
+#: flwr.client.client.Client.get_properties:7 of
+msgid "The current client properties."
+msgstr ""
+
+#: flwr.client.client.Client.to_client:1 of
+msgid "Return client (itself)."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:24
+msgid "start_client"
+msgstr ""
+
+#: flwr.client.app.start_client:1 of
+msgid "Start a Flower client node which connects to a Flower server."
+msgstr ""
+
+#: flwr.client.app.start_client:3 flwr.client.app.start_numpy_client:3 of
+msgid ""
+"The IPv4 or IPv6 address of the server. If the Flower server runs on the "
+"same machine on port 8080, then `server_address` would be "
+"`\"[::]:8080\"`."
+msgstr ""
+
+#: flwr.client.app.start_client:7 of
+msgid "..."
+msgstr ""
+
+#: flwr.client.app.start_client:9 of
+msgid "A callable that instantiates a Client. (default: None)"
+msgstr ""
+
+#: flwr.client.app.start_client:11 of
+msgid ""
+"An implementation of the abstract base class `flwr.client.Client` "
+"(default: None)"
+msgstr ""
+
+#: flwr.client.app.start_client:14 flwr.client.app.start_numpy_client:9 of
+msgid ""
+"The maximum length of gRPC messages that can be exchanged with the Flower"
+" server. The default should be sufficient for most models. Users who "
+"train very large models might need to increase this value. Note that the "
+"Flower server needs to be started with the same value (see "
+"`flwr.server.start_server`), otherwise it will not know about the "
+"increased limit and block larger messages."
+msgstr ""
+
+#: flwr.client.app.start_client:21 flwr.client.app.start_numpy_client:16 of
+msgid ""
+"The PEM-encoded root certificates as a byte string or a path string. If "
+"provided, a secure connection using the certificates will be established "
+"to an SSL-enabled Flower server."
+msgstr ""
+
+#: flwr.client.app.start_client:25 flwr.client.app.start_numpy_client:20 of
+msgid ""
+"Configure the transport layer. Allowed values: - 'grpc-bidi': gRPC, "
+"bidirectional streaming - 'grpc-rere': gRPC, request-response "
+"(experimental) - 'rest': HTTP (experimental)"
+msgstr ""
+
+#: flwr.client.app.start_client:32 flwr.client.app.start_numpy_client:27
+#: flwr.server.app.start_server:41 of
+msgid "Examples"
+msgstr ""
+
+#: flwr.client.app.start_client:33 of
+msgid "Starting a gRPC client with an insecure server connection:"
+msgstr ""
+
+#: flwr.client.app.start_client:43 flwr.client.app.start_numpy_client:35 of
+msgid "Starting an SSL-enabled gRPC client:"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:32
+msgid "NumPyClient"
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient:1 of
+msgid "Abstract base class for Flower clients using NumPy."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:3
+#: flwr.client.numpy_client.NumPyClient.fit:3
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:5
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:8
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:5
+#: flwr.server.strategy.strategy.Strategy.configure_fit:5
+#: flwr.server.strategy.strategy.Strategy.evaluate:8 of
+msgid "The current (global) model parameters."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:5 of
+msgid ""
+"Configuration parameters which allow the server to influence evaluation "
+"on the client. It can be used to communicate arbitrary values from the "
+"server to the client, for example, to influence the number of examples "
+"used for evaluation."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:11 of
+msgid ""
+"* **loss** (*float*) -- The evaluation loss of the model on the local "
+"dataset. * **num_examples** (*int*) -- The number of examples used for "
+"evaluation. * **metrics** (*Dict[str, Scalar]*) -- A dictionary mapping "
+"arbitrary string keys to values of   type bool, bytes, float, int, or "
+"str. It can be used to   communicate arbitrary values back to the server."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:11 of
+msgid ""
+"**loss** (*float*) -- The evaluation loss of the model on the local "
+"dataset."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:12 of
+msgid "**num_examples** (*int*) -- The number of examples used for evaluation."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:13
+#: flwr.client.numpy_client.NumPyClient.fit:13 of
+msgid ""
+"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary "
+"string keys to values of type bool, bytes, float, int, or str. It can be "
+"used to communicate arbitrary values back to the server."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:19 of
+msgid ""
+"The previous return type format (int, float, float) and the extended "
+"format (int, float, float, Dict[str, Scalar]) have been deprecated and "
+"removed since Flower 0.19."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:1 of
+msgid "Train the provided parameters using the locally held dataset."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:5 of
+msgid ""
+"Configuration parameters which allow the server to influence training on "
+"the client. It can be used to communicate arbitrary values from the "
+"server to the client, for example, to set the number of (local) training "
+"epochs."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:11 of
+msgid ""
+"* **parameters** (*NDArrays*) -- The locally updated model parameters. * "
+"**num_examples** (*int*) -- The number of examples used for training. * "
+"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary "
+"string keys to values of type   bool, bytes, float, int, or str. It can "
+"be used to communicate   arbitrary values back to the server."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:11 of
+msgid "**parameters** (*NDArrays*) -- The locally updated model parameters."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:12 of
+msgid "**num_examples** (*int*) -- The number of examples used for training."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_parameters:3 of
+msgid ""
+"Configuration parameters requested by the server. This can be used to "
+"tell the client which parameters are needed along with some Scalar "
+"attributes."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_parameters:8 of
+msgid "**parameters** -- The local model parameters as a list of NumPy ndarrays."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_properties:1 of
+msgid "Return a client's set of properties."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_properties:3 of
+msgid ""
+"Configuration parameters requested by the server. This can be used to "
+"tell the client which properties are needed along with some Scalar "
+"attributes."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_properties:8 of
+msgid ""
+"**properties** -- A dictionary mapping arbitrary string keys to values of"
+" type bool, bytes, float, int, or str. It can be used to communicate "
+"arbitrary property values back to the server."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.to_client:1 of
+msgid "Convert to object to Client type and return it."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:41
+msgid "start_numpy_client"
+msgstr ""
+
+#: flwr.client.app.start_numpy_client:1 of
+msgid "Start a Flower NumPyClient which connects to a gRPC server."
+msgstr ""
+
+#: flwr.client.app.start_numpy_client:7 of
+msgid "An implementation of the abstract base class `flwr.client.NumPyClient`."
+msgstr ""
+
+#: flwr.client.app.start_numpy_client:28 of
+msgid "Starting a client with an insecure server connection:"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:49
+msgid "start_simulation"
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:1 of
+msgid "Start a Ray-based Flower simulation server."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:3 of
+msgid ""
+"A function creating client instances. The function must take a single "
+"`str` argument called `cid`. It should return a single client instance of"
+" type Client. Note that the created client instances are ephemeral and "
+"will often be destroyed after a single method invocation. Since client "
+"instances are not long-lived, they should not attempt to carry state over"
+" method invocations. Any state required by the instance (model, dataset, "
+"hyperparameters, ...) should be (re-)created in either the call to "
+"`client_fn` or the call to any of the client methods (e.g., load "
+"evaluation data in the `evaluate` method itself)."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:13 of
+msgid ""
+"The total number of clients in this simulation. This must be set if "
+"`clients_ids` is not set and vice-versa."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:16 of
+msgid ""
+"List `client_id`s for each client. This is only required if `num_clients`"
+" is not set. Setting both `num_clients` and `clients_ids` with "
+"`len(clients_ids)` not equal to `num_clients` generates an error."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:20 of
+msgid ""
+"\"num_gpus\": 0.0}` CPU and GPU resources for a single client. Supported "
+"keys are `num_cpus` and `num_gpus`. To understand the GPU utilization "
+"caused by `num_gpus`, as well as using custom resources, please consult "
+"the Ray documentation."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:25 of
+msgid ""
+"An implementation of the abstract base class `flwr.server.Server`. If no "
+"instance is provided, then `start_server` will create one."
+msgstr ""
+
+#: flwr.server.app.start_server:9 flwr.simulation.app.start_simulation:28 of
+msgid ""
+"Currently supported values are `num_rounds` (int, default: 1) and "
+"`round_timeout` in seconds (float, default: None)."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:31 of
+msgid ""
+"An implementation of the abstract base class `flwr.server.Strategy`. If "
+"no strategy is provided, then `start_server` will use "
+"`flwr.server.strategy.FedAvg`."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:35 of
+msgid ""
+"An implementation of the abstract base class `flwr.server.ClientManager`."
+" If no implementation is provided, then `start_simulation` will use "
+"`flwr.server.client_manager.SimpleClientManager`."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:39 of
+msgid ""
+"Optional dictionary containing arguments for the call to `ray.init`. If "
+"ray_init_args is None (the default), Ray will be initialized with the "
+"following default args:  { \"ignore_reinit_error\": True, "
+"\"include_dashboard\": False }  An empty dictionary can be used "
+"(ray_init_args={}) to prevent any arguments from being passed to "
+"ray.init."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:39 of
+msgid ""
+"Optional dictionary containing arguments for the call to `ray.init`. If "
+"ray_init_args is None (the default), Ray will be initialized with the "
+"following default args:"
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:43 of
+msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }"
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:45 of
+msgid ""
+"An empty dictionary can be used (ray_init_args={}) to prevent any "
+"arguments from being passed to ray.init."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:48 of
+msgid ""
+"Set to True to prevent `ray.shutdown()` in case "
+"`ray.is_initialized()=True`."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:50 of
+msgid ""
+"Optionally specify the type of actor to use. The actor object, which "
+"persists throughout the simulation, will be the process in charge of "
+"running the clients' jobs (i.e. their `fit()` method)."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:54 of
+msgid ""
+"If you want to create your own Actor classes, you might need to pass some"
+" input argument. You can use this dictionary for such purpose."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:57 of
+msgid ""
+"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for "
+"the VCE to choose in which node the actor is placed. If you are an "
+"advanced user needed more control you can use lower-level scheduling "
+"strategies to pin actors to specific compute nodes (e.g. via "
+"NodeAffinitySchedulingStrategy). Please note this is an advanced feature."
+" For all details, please refer to the Ray documentation: "
+"https://docs.ray.io/en/latest/ray-core/scheduling/index.html"
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:66 of
+msgid "**hist** -- Object containing metrics from training."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:57
+msgid "server"
+msgstr ""
+
+#: flwr.server:1 of
+msgid "Flower server."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:65
+msgid "server.start_server"
+msgstr ""
+
+#: flwr.server.app.start_server:1 of
+msgid "Start a Flower server using the gRPC transport layer."
+msgstr ""
+
+#: flwr.server.app.start_server:3 of
+msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`."
+msgstr ""
+
+#: flwr.server.app.start_server:5 of
+msgid ""
+"A server implementation, either `flwr.server.Server` or a subclass "
+"thereof. If no instance is provided, then `start_server` will create one."
+msgstr ""
+
+#: flwr.server.app.start_server:12 of
+msgid ""
+"An implementation of the abstract base class "
+"`flwr.server.strategy.Strategy`. If no strategy is provided, then "
+"`start_server` will use `flwr.server.strategy.FedAvg`."
+msgstr ""
+
+#: flwr.server.app.start_server:16 of
+msgid ""
+"An implementation of the abstract base class `flwr.server.ClientManager`."
+" If no implementation is provided, then `start_server` will use "
+"`flwr.server.client_manager.SimpleClientManager`."
+msgstr ""
+
+#: flwr.server.app.start_server:21 of
+msgid ""
+"The maximum length of gRPC messages that can be exchanged with the Flower"
+" clients. The default should be sufficient for most models. Users who "
+"train very large models might need to increase this value. Note that the "
+"Flower clients need to be started with the same value (see "
+"`flwr.client.start_client`), otherwise clients will not know about the "
+"increased limit and block larger messages."
+msgstr ""
+
+#: flwr.server.app.start_server:28 of
+msgid ""
+"Tuple containing root certificate, server certificate, and private key to"
+" start a secure SSL-enabled server. The tuple is expected to have three "
+"bytes elements in the following order:      * CA certificate.     * "
+"server certificate.     * server private key."
+msgstr ""
+
+#: flwr.server.app.start_server:28 of
+msgid ""
+"Tuple containing root certificate, server certificate, and private key to"
+" start a secure SSL-enabled server. The tuple is expected to have three "
+"bytes elements in the following order:"
+msgstr ""
+
+#: flwr.server.app.start_server:32 of
+msgid "CA certificate."
+msgstr ""
+
+#: flwr.server.app.start_server:33 of
+msgid "server certificate."
+msgstr ""
+
+#: flwr.server.app.start_server:34 of
+msgid "server private key."
+msgstr ""
+
+#: flwr.server.app.start_server:37 of
+msgid "**hist** -- Object containing training and evaluation metrics."
+msgstr ""
+
+#: flwr.server.app.start_server:42 of
+msgid "Starting an insecure server:"
+msgstr ""
+
+#: flwr.server.app.start_server:46 of
+msgid "Starting an SSL-enabled server:"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:73
+msgid "server.strategy"
+msgstr ""
+
+#: flwr.server.strategy:1 of
+msgid "Contains the strategy abstraction and different implementations."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:81
+msgid "server.strategy.Strategy"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy:1 of
+msgid "Abstract base class for server strategy implementations."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 of
+msgid "Aggregate evaluation results."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:3
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:6
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:3
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:3
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:3
+#: flwr.server.strategy.strategy.Strategy.configure_fit:3
+#: flwr.server.strategy.strategy.Strategy.evaluate:6 of
+msgid "The current round of federated learning."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of
+msgid ""
+"Successful updates from the previously selected and configured clients. "
+"Each pair of `(ClientProxy, FitRes` constitutes a successful update from "
+"one of the previously selected clients. Not that not all previously "
+"selected clients are necessarily included in this list: a client might "
+"drop out and not submit a result. For each client that did not submit an "
+"update, there should be an `Exception` in `failures`."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:13
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:13 of
+msgid "Exceptions that occurred while the server was waiting for client updates."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of
+msgid ""
+"**aggregation_result** -- The aggregated evaluation result. Aggregation "
+"typically uses some variant of a weighted average."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of
+msgid "Aggregate training results."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of
+msgid ""
+"Successful updates from the previously selected and configured clients. "
+"Each pair of `(ClientProxy, FitRes)` constitutes a successful update from"
+" one of the previously selected clients. Not that not all previously "
+"selected clients are necessarily included in this list: a client might "
+"drop out and not submit a result. For each client that did not submit an "
+"update, there should be an `Exception` in `failures`."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of
+msgid ""
+"**parameters** -- If parameters are returned, then the server will treat "
+"these as the new global model parameters (i.e., it will replace the "
+"previous parameters with the ones returned from this method). If `None` "
+"is returned (e.g., because there were only failures and no viable "
+"results) then the server will no update the previous model parameters, "
+"the updates received in this round are discarded, and the global model "
+"parameters remain the same."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of
+msgid "Configure the next round of evaluation."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:7
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:10
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:7
+#: flwr.server.strategy.strategy.Strategy.configure_fit:7
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters:3 of
+msgid "The client manager which holds all currently connected clients."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:10
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:10 of
+msgid ""
+"**evaluate_configuration** -- A list of tuples. Each tuple in the list "
+"identifies a `ClientProxy` and the `EvaluateIns` for this particular "
+"`ClientProxy`. If a particular `ClientProxy` is not included in this "
+"list, it means that this `ClientProxy` will not participate in the next "
+"round of federated evaluation."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1
+#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1
+#: flwr.server.strategy.fedprox.FedProx.configure_fit:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1
+#: flwr.server.strategy.strategy.Strategy.configure_fit:1 of
+msgid "Configure the next round of training."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13
+#: flwr.server.strategy.strategy.Strategy.configure_fit:10 of
+msgid ""
+"**fit_configuration** -- A list of tuples. Each tuple in the list "
+"identifies a `ClientProxy` and the `FitIns` for this particular "
+"`ClientProxy`. If a particular `ClientProxy` is not included in this "
+"list, it means that this `ClientProxy` will not participate in the next "
+"round of federated learning."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.evaluate:1 of
+msgid "Evaluate the current model parameters."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.evaluate:3 of
+msgid ""
+"This function can be used to perform centralized (i.e., server-side) "
+"evaluation of model parameters."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.evaluate:11 of
+msgid ""
+"**evaluation_result** -- The evaluation result, usually a Tuple "
+"containing loss and a dictionary containing task-specific metrics (e.g., "
+"accuracy)."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of
+msgid "Initialize the (global) model parameters."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of
+msgid ""
+"**parameters** -- If parameters are returned, then the server will treat "
+"these as the initial global model parameters."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:90
+msgid "server.strategy.FedAvg"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg:1 of
+msgid "Configurable FedAvg strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:1
+#: flwr.server.strategy.fedavg.FedAvg.__init__:1
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:1 of
+msgid "Federated Averaging strategy."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:3
+#: flwr.server.strategy.fedavg.FedAvg.__init__:3
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/1602.05629"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:5
+#: flwr.server.strategy.fedavg.FedAvg.__init__:5
+#: flwr.server.strategy.fedprox.FedProx.__init__:37
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:5 of
+msgid ""
+"Fraction of clients used during training. In case `min_fit_clients` is "
+"larger than `fraction_fit * available_clients`, `min_fit_clients` will "
+"still be sampled. Defaults to 1.0."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:9
+#: flwr.server.strategy.fedavg.FedAvg.__init__:9
+#: flwr.server.strategy.fedprox.FedProx.__init__:41
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:9 of
+msgid ""
+"Fraction of clients used during validation. In case "
+"`min_evaluate_clients` is larger than `fraction_evaluate * "
+"available_clients`, `min_evaluate_clients` will still be sampled. "
+"Defaults to 1.0."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:9
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:13
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:9
+#: flwr.server.strategy.fedadam.FedAdam.__init__:9
+#: flwr.server.strategy.fedavg.FedAvg.__init__:13
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:9
+#: flwr.server.strategy.fedopt.FedOpt.__init__:9
+#: flwr.server.strategy.fedprox.FedProx.__init__:45
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:7
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:9
+#: flwr.server.strategy.krum.Krum.__init__:7
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:13 of
+msgid "Minimum number of clients used during training. Defaults to 2."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:11
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:15
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:11
+#: flwr.server.strategy.fedadam.FedAdam.__init__:11
+#: flwr.server.strategy.fedavg.FedAvg.__init__:15
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:11
+#: flwr.server.strategy.fedopt.FedOpt.__init__:11
+#: flwr.server.strategy.fedprox.FedProx.__init__:47
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:9
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:11
+#: flwr.server.strategy.krum.Krum.__init__:9
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:15 of
+msgid "Minimum number of clients used during validation. Defaults to 2."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:13
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:17
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:13
+#: flwr.server.strategy.fedadam.FedAdam.__init__:13
+#: flwr.server.strategy.fedavg.FedAvg.__init__:17
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:13
+#: flwr.server.strategy.fedopt.FedOpt.__init__:13
+#: flwr.server.strategy.fedprox.FedProx.__init__:49
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:11
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:13
+#: flwr.server.strategy.krum.Krum.__init__:11
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:17 of
+msgid "Minimum number of total clients in the system. Defaults to 2."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:17
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:19
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:15
+#: flwr.server.strategy.fedadam.FedAdam.__init__:15
+#: flwr.server.strategy.fedavg.FedAvg.__init__:19
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:15
+#: flwr.server.strategy.fedopt.FedOpt.__init__:15
+#: flwr.server.strategy.fedprox.FedProx.__init__:51
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:13
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:15
+#: flwr.server.strategy.krum.Krum.__init__:18
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:19 of
+msgid "Optional function used for validation. Defaults to None."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:19
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:21
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:17
+#: flwr.server.strategy.fedadam.FedAdam.__init__:17
+#: flwr.server.strategy.fedavg.FedAvg.__init__:21
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:17
+#: flwr.server.strategy.fedopt.FedOpt.__init__:17
+#: flwr.server.strategy.fedprox.FedProx.__init__:53
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:15
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:17
+#: flwr.server.strategy.krum.Krum.__init__:20
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:21 of
+msgid "Function used to configure training. Defaults to None."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:21
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:23
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:19
+#: flwr.server.strategy.fedadam.FedAdam.__init__:19
+#: flwr.server.strategy.fedavg.FedAvg.__init__:23
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:19
+#: flwr.server.strategy.fedopt.FedOpt.__init__:19
+#: flwr.server.strategy.fedprox.FedProx.__init__:55
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:17
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:19
+#: flwr.server.strategy.krum.Krum.__init__:22
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:23 of
+msgid "Function used to configure validation. Defaults to None."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:23
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:25
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:25
+#: flwr.server.strategy.fedadam.FedAdam.__init__:21
+#: flwr.server.strategy.fedavg.FedAvg.__init__:25
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:21
+#: flwr.server.strategy.fedopt.FedOpt.__init__:21
+#: flwr.server.strategy.fedprox.FedProx.__init__:57
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:19
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:21
+#: flwr.server.strategy.krum.Krum.__init__:24
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:25 of
+msgid "Whether or not accept rounds containing failures. Defaults to True."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:25
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:27
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:27
+#: flwr.server.strategy.fedadam.FedAdam.__init__:23
+#: flwr.server.strategy.fedavg.FedAvg.__init__:27
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:23
+#: flwr.server.strategy.fedopt.FedOpt.__init__:23
+#: flwr.server.strategy.fedprox.FedProx.__init__:59
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:21
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:23
+#: flwr.server.strategy.krum.Krum.__init__:26
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:27 of
+msgid "Initial global model parameters."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:29
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:31
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:21
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:23
+#: flwr.server.strategy.fedadam.FedAdam.__init__:25
+#: flwr.server.strategy.fedadam.FedAdam.__init__:27
+#: flwr.server.strategy.fedavg.FedAvg.__init__:29
+#: flwr.server.strategy.fedavg.FedAvg.__init__:31
+#: flwr.server.strategy.fedopt.FedOpt.__init__:25
+#: flwr.server.strategy.fedopt.FedOpt.__init__:27
+#: flwr.server.strategy.fedprox.FedProx.__init__:61
+#: flwr.server.strategy.fedprox.FedProx.__init__:63
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:25
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:27
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:29
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:31 of
+msgid "Metrics aggregation function, optional."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1 of
+msgid "Aggregate evaluation losses using weighted average."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_fit:1
+#: flwr.server.strategy.fedadagrad.FedAdagrad.aggregate_fit:1
+#: flwr.server.strategy.fedadam.FedAdam.aggregate_fit:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1
+#: flwr.server.strategy.fedavgm.FedAvgM.aggregate_fit:1
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.aggregate_fit:1
+#: flwr.server.strategy.fedyogi.FedYogi.aggregate_fit:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_fit:1 of
+msgid "Aggregate fit results using weighted average."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.evaluate:1
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1 of
+msgid "Evaluate model parameters using an evaluation function."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1
+#: flwr.server.strategy.fedavgm.FedAvgM.initialize_parameters:1 of
+msgid "Initialize global model parameters."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1
+#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of
+msgid "Use a fraction of available clients for evaluation."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1
+#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of
+msgid "Return the sample size and the required number of available clients."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:101
+msgid "server.strategy.FedAvgM"
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM:1
+#: flwr.server.strategy.fedmedian.FedMedian:1 of
+msgid "Configurable FedAvg with Momentum strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:1 of
+msgid "Federated Averaging with Momentum strategy."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:3 of
+msgid "Implementation based on https://arxiv.org/pdf/1909.06335.pdf"
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:5
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:3
+#: flwr.server.strategy.krum.Krum.__init__:3 of
+msgid "Fraction of clients used during training. Defaults to 0.1."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:7
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:5
+#: flwr.server.strategy.krum.Krum.__init__:5 of
+msgid "Fraction of clients used during validation. Defaults to 0.1."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:25 of
+msgid ""
+"Server-side learning rate used in server-side optimization. Defaults to "
+"1.0."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:28 of
+msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:112
+msgid "server.strategy.FedMedian"
+msgstr ""
+
+#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of
+msgid "Aggregate fit results using median."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:122
+msgid "server.strategy.QFedAvg"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg:1 of
+msgid "Configurable QFedAvg strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:133
+msgid "server.strategy.FaultTolerantFedAvg"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of
+msgid "Configurable fault-tolerant FedAvg strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:144
+msgid "server.strategy.FedOpt"
+msgstr ""
+
+#: flwr.server.strategy.fedopt.FedOpt:1 of
+msgid "Configurable FedAdagrad strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fedopt.FedOpt.__init__:1 of
+msgid "Federated Optim strategy interface."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:3
+#: flwr.server.strategy.fedadam.FedAdam.__init__:3
+#: flwr.server.strategy.fedopt.FedOpt.__init__:3
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/2003.00295v5"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:5
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:5
+#: flwr.server.strategy.fedadam.FedAdam.__init__:5
+#: flwr.server.strategy.fedopt.FedOpt.__init__:5
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:5 of
+msgid "Fraction of clients used during training. Defaults to 1.0."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:7
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:7
+#: flwr.server.strategy.fedadam.FedAdam.__init__:7
+#: flwr.server.strategy.fedopt.FedOpt.__init__:7
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:7 of
+msgid "Fraction of clients used during validation. Defaults to 1.0."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:29
+#: flwr.server.strategy.fedadam.FedAdam.__init__:29
+#: flwr.server.strategy.fedopt.FedOpt.__init__:29
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:29 of
+msgid "Server-side learning rate. Defaults to 1e-1."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:31
+#: flwr.server.strategy.fedadam.FedAdam.__init__:31
+#: flwr.server.strategy.fedopt.FedOpt.__init__:31
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:31 of
+msgid "Client-side learning rate. Defaults to 1e-1."
+msgstr ""
+
+#: flwr.server.strategy.fedopt.FedOpt.__init__:33 of
+msgid "Momentum parameter. Defaults to 0.0."
+msgstr ""
+
+#: flwr.server.strategy.fedopt.FedOpt.__init__:35 of
+msgid "Second moment parameter. Defaults to 0.0."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:33
+#: flwr.server.strategy.fedadam.FedAdam.__init__:37
+#: flwr.server.strategy.fedopt.FedOpt.__init__:37
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:37 of
+msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:155
+msgid "server.strategy.FedProx"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx:1 of
+msgid "Configurable FedProx strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:1 of
+msgid "Federated Optimization strategy."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/1812.06127"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:5 of
+msgid ""
+"The strategy in itself will not be different than FedAvg, the client "
+"needs to be adjusted. A proximal term needs to be added to the loss "
+"function during the training:"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:9 of
+msgid ""
+"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n"
+"\n"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:12 of
+msgid ""
+"Where $w^t$ are the global parameters and $w$ are the local weights the "
+"function will be optimized with."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:15 of
+msgid "In PyTorch, for example, the loss would go from:"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:21 of
+msgid "To:"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:30 of
+msgid ""
+"With `global_params` being a copy of the parameters before the training "
+"takes place."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:65 of
+msgid ""
+"The weight of the proximal term used in the optimization. 0.0 makes this "
+"strategy equivalent to FedAvg, and the higher the coefficient, the more "
+"regularization will be used (that is, the client parameters will need to "
+"be closer to the server parameters during training)."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of
+msgid "Sends the proximal factor mu to the clients"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:166
+msgid "server.strategy.FedAdagrad"
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of
+msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad:3
+#: flwr.server.strategy.fedadam.FedAdam:3
+#: flwr.server.strategy.fedyogi.FedYogi:5 of
+msgid "Paper: https://arxiv.org/abs/2003.00295"
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:1
+#: flwr.server.strategy.fedadam.FedAdam.__init__:1 of
+msgid "Federated learning strategy using Adagrad on server-side."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:177
+msgid "server.strategy.FedAdam"
+msgstr ""
+
+#: flwr.server.strategy.fedadam.FedAdam:1 of
+msgid "FedAdam - Adaptive Federated Optimization using Adam."
+msgstr ""
+
+#: flwr.server.strategy.fedadam.FedAdam.__init__:33
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:33 of
+msgid "Momentum parameter. Defaults to 0.9."
+msgstr ""
+
+#: flwr.server.strategy.fedadam.FedAdam.__init__:35
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:35 of
+msgid "Second moment parameter. Defaults to 0.99."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:188
+msgid "server.strategy.FedYogi"
+msgstr ""
+
+#: flwr.server.strategy.fedyogi.FedYogi:1 of
+msgid "FedYogi [Reddi et al., 2020] strategy."
+msgstr ""
+
+#: flwr.server.strategy.fedyogi.FedYogi:3 of
+msgid "Adaptive Federated Optimization using Yogi."
+msgstr ""
+
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:1 of
+msgid "Federated learning strategy using Yogi on server-side."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:199
+msgid "server.strategy.FedTrimmedAvg"
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:1 of
+msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]."
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of
+msgid "Paper: https://arxiv.org/abs/1803.01498"
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:23 of
+msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2."
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of
+msgid "Aggregate fit results using trimmed average."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:210
+msgid "server.strategy.Krum"
+msgstr ""
+
+#: flwr.server.strategy.krum.Krum:1 of
+msgid "Configurable Krum strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.krum.Krum.__init__:1 of
+msgid "Krum strategy."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:15
+#: flwr.server.strategy.krum.Krum.__init__:13 of
+msgid "Number of malicious clients in the system. Defaults to 0."
+msgstr ""
+
+#: flwr.server.strategy.krum.Krum.__init__:15 of
+msgid ""
+"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in"
+" that case classical Krum is applied."
+msgstr ""
+
+#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of
+msgid "Aggregate fit results using Krum."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:220
+msgid "server.strategy.Bulyan"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan:1 of
+msgid "Bulyan strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:1 of
+msgid "Bulyan strategy."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/1802.07927."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:27 of
+msgid ""
+"Byzantine resilient aggregation rule that is used as the first step of "
+"the Bulyan (e.g., Krum)"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:29 of
+msgid "arguments to the first_aggregation rule"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1 of
+msgid "Aggregate fit results using Bulyan."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:231
+msgid "server.strategy.FedXgbNnAvg"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of
+msgid "Configurable FedXgbNnAvg strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.__init__:1 of
+msgid "Federated XGBoost [Ma et al., 2023] strategy."
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/2304.07537."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:242
+msgid "server.strategy.DPFedAvgAdaptive"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of
+msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 of
+msgid "Aggregate training results as in DPFedAvgFixed and update clip norms."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:253
+msgid "server.strategy.DPFedAvgFixed"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of
+msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1 of
+msgid "Aggregate evaluation losses using the given strategy."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of
+msgid "Aggregate training results using unweighted aggregation."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of
+msgid "Configure the next round of evaluation using the specified strategy."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of
+msgid ""
+"Configure the next round of training incorporating Differential Privacy "
+"(DP)."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of
+msgid ""
+"Configuration of the next training round includes information related to "
+"DP, such as clip norm and noise stddev."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of
+msgid "Evaluate model parameters using an evaluation function from the strategy."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of
+msgid "Initialize global model parameters using given strategy."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:261
+msgid "common"
+msgstr ""
+
+#: flwr.common:1 of
+msgid "Common components shared between server and client."
+msgstr ""
+
+#: flwr.common.typing.ClientMessage:1 of
+msgid "ClientMessage is a container used to hold one result message."
+msgstr ""
+
+#: flwr.common.typing.Code:1 of
+msgid "Client status codes."
+msgstr ""
+
+#: flwr.common.typing.DisconnectRes:1 of
+msgid "DisconnectRes message from client to server."
+msgstr ""
+
+#: flwr.common.typing.EvaluateIns:1 of
+msgid "Evaluate instructions for a client."
+msgstr ""
+
+#: flwr.common.typing.EvaluateRes:1 of
+msgid "Evaluate response from a client."
+msgstr ""
+
+#: flwr.common.telemetry.EventType:1 of
+msgid "Types of telemetry events."
+msgstr ""
+
+#: flwr.common.typing.FitIns:1 of
+msgid "Fit instructions for a client."
+msgstr ""
+
+#: flwr.common.typing.FitRes:1 of
+msgid "Fit response from a client."
+msgstr ""
+
+#: flwr.common.typing.GetParametersIns:1 of
+msgid "Parameters request for a client."
+msgstr ""
+
+#: flwr.common.typing.GetParametersRes:1 of
+msgid "Response when asked to return parameters."
+msgstr ""
+
+#: flwr.common.typing.GetPropertiesIns:1 of
+msgid "Properties request for a client."
+msgstr ""
+
+#: flwr.common.typing.GetPropertiesRes:1 of
+msgid "Properties response from a client."
+msgstr ""
+
+#: flwr.common.typing.Parameters:1 of
+msgid "Model parameters."
+msgstr ""
+
+#: flwr.common.typing.ReconnectIns:1 of
+msgid "ReconnectIns message from server to client."
+msgstr ""
+
+#: flwr.common.typing.ServerMessage:1 of
+msgid "ServerMessage is a container used to hold one instruction message."
+msgstr ""
+
+#: flwr.common.typing.Status:1 of
+msgid "Client status."
+msgstr ""
+
+#: flwr.common.parameter.bytes_to_ndarray:1 of
+msgid "Deserialize NumPy ndarray from bytes."
+msgstr ""
+
+#: flwr.common.logger.configure:1 of
+msgid "Configure logging to file and/or remote log server."
+msgstr ""
+
+#: logging.Logger.log:1 of
+msgid "Log 'msg % args' with the integer severity 'level'."
+msgstr ""
+
+#: logging.Logger.log:3 of
+msgid ""
+"To pass exception information, use the keyword argument exc_info with a "
+"true value, e.g."
+msgstr ""
+
+#: logging.Logger.log:6 of
+#, python-format
+msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)"
+msgstr ""
+
+#: flwr.common.parameter.ndarray_to_bytes:1 of
+msgid "Serialize NumPy ndarray to bytes."
+msgstr ""
+
+#: flwr.common.parameter.ndarrays_to_parameters:1 of
+msgid "Convert NumPy ndarrays to parameters object."
+msgstr ""
+
+#: flwr.common.date.now:1 of
+msgid "Construct a datetime from time.time() with time zone set to UTC."
+msgstr ""
+
+#: flwr.common.parameter.parameters_to_ndarrays:1 of
+msgid "Convert parameters object to NumPy ndarrays."
+msgstr ""
+
+#: ../../source/ref-changelog.md:1
+msgid "Changelog"
+msgstr ""
+
+#: ../../source/ref-changelog.md:3
+msgid "Unreleased"
+msgstr ""
+
+#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:83
+#: ../../source/ref-changelog.md:167 ../../source/ref-changelog.md:231
+#: ../../source/ref-changelog.md:289 ../../source/ref-changelog.md:358
+#: ../../source/ref-changelog.md:487 ../../source/ref-changelog.md:529
+#: ../../source/ref-changelog.md:596 ../../source/ref-changelog.md:662
+#: ../../source/ref-changelog.md:707 ../../source/ref-changelog.md:746
+#: ../../source/ref-changelog.md:779 ../../source/ref-changelog.md:829
+msgid "What's new?"
+msgstr ""
+
+#: ../../source/ref-changelog.md:7
+msgid ""
+"**Add experimental support for Python 3.12** "
+"([#2565](https://github.com/adap/flower/pull/2565))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:9
+msgid ""
+"**Support custom** `ClientManager` **in** `start_driver()` "
+"([#2292](https://github.com/adap/flower/pull/2292))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:11
+msgid ""
+"**Update REST API to support create and delete nodes** "
+"([#2283](https://github.com/adap/flower/pull/2283))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:13
+msgid ""
+"**Update the C++ SDK** "
+"([#2537](https://github/com/adap/flower/pull/2537), "
+"[#2528](https://github/com/adap/flower/pull/2528), "
+"[#2523](https://github.com/adap/flower/pull/2523), "
+"[#2522](https://github.com/adap/flower/pull/2522))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:15
+msgid "Add gRPC request-response capability to the C++ SDK."
+msgstr ""
+
+#: ../../source/ref-changelog.md:17
+msgid ""
+"**Fix the incorrect return types of Strategy** "
+"([#2432](https://github.com/adap/flower/pull/2432/files))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:19
+msgid ""
+"The types of the return values in the docstrings in two methods "
+"(`aggregate_fit` and `aggregate_evaluate`) now match the hint types in "
+"the code."
+msgstr ""
+
+#: ../../source/ref-changelog.md:21
+msgid ""
+"**Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), "
+"[#2390](https://github.com/adap/flower/pull/2390), "
+"[#2493](https://github.com/adap/flower/pull/2493))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:23
+msgid ""
+"Using the `client_fn`, Flower clients can interchangeably run as "
+"standalone processes (i.e. via `start_client`) or in simulation (i.e. via"
+" `start_simulation`) without requiring changes to how the client class is"
+" defined and instantiated. Calling `start_numpy_client` is now "
+"deprecated."
+msgstr ""
+
+#: ../../source/ref-changelog.md:25
+msgid "**Update Flower Baselines**"
+msgstr ""
+
+#: ../../source/ref-changelog.md:27
+msgid ""
+"FedProx ([#2210](https://github.com/adap/flower/pull/2210), "
+"[#2286](https://github.com/adap/flower/pull/2286), "
+"[#2509](https://github.com/adap/flower/pull/2509))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:29
+msgid ""
+"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), "
+"[#2400](https://github.com/adap/flower/pull/2400))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:31
+msgid ""
+"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), "
+"[#2507](https://github.com/adap/flower/pull/2507))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:33
+msgid ""
+"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), "
+"[#2508](https://github.com/adap/flower/pull/2508))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:35
+msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:37
+msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:39
+msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:41
+msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:43
+msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:45
+msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:47
+msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:49
+msgid ""
+"FedBN ([#2608](https://github.com/adap/flower/pull/2608), "
+"[#2615](https://github.com/adap/flower/pull/2615))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:51
+msgid ""
+"**Update Flower Examples** "
+"([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425),"
+" [#2526](https://github.com/adap/flower/pull/2526))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:53
+msgid ""
+"**General updates to baselines** "
+"([#2301](https://github.com/adap/flower/pull/2301), "
+"[#2305](https://github.com/adap/flower/pull/2305), "
+"[#2307](https://github.com/adap/flower/pull/2307), "
+"[#2327](https://github.com/adap/flower/pull/2327), "
+"[#2435](https://github.com/adap/flower/pull/2435))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:55
+msgid ""
+"**General updates to the simulation engine** "
+"([#2331](https://github.com/adap/flower/pull/2331), "
+"[#2447](https://github.com/adap/flower/pull/2447), "
+"[#2448](https://github.com/adap/flower/pull/2448))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:57
+msgid ""
+"**General improvements** "
+"([#2309](https://github.com/adap/flower/pull/2309), "
+"[#2310](https://github.com/adap/flower/pull/2310), "
+"[2313](https://github.com/adap/flower/pull/2313), "
+"[#2316](https://github.com/adap/flower/pull/2316), "
+"[2317](https://github.com/adap/flower/pull/2317),[#2349](https://github.com/adap/flower/pull/2349),"
+" [#2360](https://github.com/adap/flower/pull/2360), "
+"[#2402](https://github.com/adap/flower/pull/2402), "
+"[#2446](https://github.com/adap/flower/pull/2446) "
+"[#2561](https://github.com/adap/flower/pull/2561))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:59 ../../source/ref-changelog.md:153
+#: ../../source/ref-changelog.md:217 ../../source/ref-changelog.md:271
+#: ../../source/ref-changelog.md:338
+msgid "Flower received many improvements under the hood, too many to list here."
+msgstr ""
+
+#: ../../source/ref-changelog.md:61
+msgid ""
+"**Add new** `Bulyan` **strategy** "
+"([#1817](https://github.com/adap/flower/pull/1817), "
+"[#1891](https://github.com/adap/flower/pull/1891))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:63
+msgid ""
+"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., "
+"2018](https://arxiv.org/abs/1802.07927)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:65 ../../source/ref-changelog.md:155
+#: ../../source/ref-changelog.md:219 ../../source/ref-changelog.md:277
+#: ../../source/ref-changelog.md:346 ../../source/ref-changelog.md:408
+#: ../../source/ref-changelog.md:427 ../../source/ref-changelog.md:583
+#: ../../source/ref-changelog.md:654 ../../source/ref-changelog.md:691
+#: ../../source/ref-changelog.md:734
+msgid "Incompatible changes"
+msgstr ""
+
+#: ../../source/ref-changelog.md:67
+msgid ""
+"**Remove support for Python 3.7** "
+"([#2280](https://github.com/adap/flower/pull/2280), "
+"[#2299](https://github.com/adap/flower/pull/2299), "
+"[#2304](https://github.com/adap/flower/pull/2304), "
+"[#2306](https://github.com/adap/flower/pull/2306), "
+"[#2355](https://github.com/adap/flower/pull/2355), "
+"[#2356](https://github.com/adap/flower/pull/2356))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:69
+msgid ""
+"Python 3.7 support was deprecated in Flower 1.5, and this release removes"
+" support. Flower now requires Python 3.8."
+msgstr ""
+
+#: ../../source/ref-changelog.md:71
+msgid ""
+"**Remove experimental argument** `rest` **from** `start_client` "
+"([#2324](https://github.com/adap/flower/pull/2324))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:73
+msgid ""
+"The (still experimental) argument `rest` was removed from `start_client` "
+"and `start_numpy_client`. Use `transport=\"rest\"` to opt into the "
+"experimental REST API instead."
+msgstr ""
+
+#: ../../source/ref-changelog.md:75
+msgid "v1.5.0 (2023-08-31)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:77 ../../source/ref-changelog.md:161
+#: ../../source/ref-changelog.md:225 ../../source/ref-changelog.md:283
+#: ../../source/ref-changelog.md:352 ../../source/ref-changelog.md:421
+msgid "Thanks to our contributors"
+msgstr ""
+
+#: ../../source/ref-changelog.md:79 ../../source/ref-changelog.md:163
+#: ../../source/ref-changelog.md:227 ../../source/ref-changelog.md:285
+msgid ""
+"We would like to give our special thanks to all the contributors who made"
+" the new version of Flower possible (in `git shortlog` order):"
+msgstr ""
+
+#: ../../source/ref-changelog.md:81
+msgid ""
+"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, "
+"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo "
+"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner "
+"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` <!---"
+"TOKEN_v1.5.0-->"
+msgstr ""
+
+#: ../../source/ref-changelog.md:85
+msgid ""
+"**Introduce new simulation engine** "
+"([#1969](https://github.com/adap/flower/pull/1969), "
+"[#2221](https://github.com/adap/flower/pull/2221), "
+"[#2248](https://github.com/adap/flower/pull/2248))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:87
+msgid ""
+"The new simulation engine has been rewritten from the ground up, yet it "
+"remains fully backwards compatible. It offers much improved stability and"
+" memory handling, especially when working with GPUs. Simulations "
+"transparently adapt to different settings to scale simulation in CPU-"
+"only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments."
+msgstr ""
+
+#: ../../source/ref-changelog.md:89
+msgid ""
+"Comprehensive documentation includes a new [how-to run "
+"simulations](https://flower.dev/docs/framework/how-to-run-"
+"simulations.html) guide, new [simulation-"
+"pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and "
+"[simulation-tensorflow](https://flower.dev/docs/examples/simulation-"
+"tensorflow.html) notebooks, and a new [YouTube tutorial "
+"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:91
+msgid ""
+"**Restructure Flower Docs** "
+"([#1824](https://github.com/adap/flower/pull/1824), "
+"[#1865](https://github.com/adap/flower/pull/1865), "
+"[#1884](https://github.com/adap/flower/pull/1884), "
+"[#1887](https://github.com/adap/flower/pull/1887), "
+"[#1919](https://github.com/adap/flower/pull/1919), "
+"[#1922](https://github.com/adap/flower/pull/1922), "
+"[#1920](https://github.com/adap/flower/pull/1920), "
+"[#1923](https://github.com/adap/flower/pull/1923), "
+"[#1924](https://github.com/adap/flower/pull/1924), "
+"[#1962](https://github.com/adap/flower/pull/1962), "
+"[#2006](https://github.com/adap/flower/pull/2006), "
+"[#2133](https://github.com/adap/flower/pull/2133), "
+"[#2203](https://github.com/adap/flower/pull/2203), "
+"[#2215](https://github.com/adap/flower/pull/2215), "
+"[#2122](https://github.com/adap/flower/pull/2122), "
+"[#2223](https://github.com/adap/flower/pull/2223), "
+"[#2219](https://github.com/adap/flower/pull/2219), "
+"[#2232](https://github.com/adap/flower/pull/2232), "
+"[#2233](https://github.com/adap/flower/pull/2233), "
+"[#2234](https://github.com/adap/flower/pull/2234), "
+"[#2235](https://github.com/adap/flower/pull/2235), "
+"[#2237](https://github.com/adap/flower/pull/2237), "
+"[#2238](https://github.com/adap/flower/pull/2238), "
+"[#2242](https://github.com/adap/flower/pull/2242), "
+"[#2231](https://github.com/adap/flower/pull/2231), "
+"[#2243](https://github.com/adap/flower/pull/2243), "
+"[#2227](https://github.com/adap/flower/pull/2227))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:93
+msgid ""
+"Much effort went into a completely restructured Flower docs experience. "
+"The documentation on [flower.dev/docs](flower.dev/docs) is now divided "
+"into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS "
+"SDK, and code example projects."
+msgstr ""
+
+#: ../../source/ref-changelog.md:95
+msgid ""
+"**Introduce Flower Swift SDK** "
+"([#1858](https://github.com/adap/flower/pull/1858), "
+"[#1897](https://github.com/adap/flower/pull/1897))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:97
+msgid ""
+"This is the first preview release of the Flower Swift SDK. Flower support"
+" on iOS is improving, and alongside the Swift SDK and code example, there"
+" is now also an iOS quickstart tutorial."
+msgstr ""
+
+#: ../../source/ref-changelog.md:99
+msgid ""
+"**Introduce Flower Android SDK** "
+"([#2131](https://github.com/adap/flower/pull/2131))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:101
+msgid ""
+"This is the first preview release of the Flower Kotlin SDK. Flower "
+"support on Android is improving, and alongside the Kotlin SDK and code "
+"example, there is now also an Android quickstart tutorial."
+msgstr ""
+
+#: ../../source/ref-changelog.md:103
+msgid ""
+"**Introduce new end-to-end testing infrastructure** "
+"([#1842](https://github.com/adap/flower/pull/1842), "
+"[#2071](https://github.com/adap/flower/pull/2071), "
+"[#2072](https://github.com/adap/flower/pull/2072), "
+"[#2068](https://github.com/adap/flower/pull/2068), "
+"[#2067](https://github.com/adap/flower/pull/2067), "
+"[#2069](https://github.com/adap/flower/pull/2069), "
+"[#2073](https://github.com/adap/flower/pull/2073), "
+"[#2070](https://github.com/adap/flower/pull/2070), "
+"[#2074](https://github.com/adap/flower/pull/2074), "
+"[#2082](https://github.com/adap/flower/pull/2082), "
+"[#2084](https://github.com/adap/flower/pull/2084), "
+"[#2093](https://github.com/adap/flower/pull/2093), "
+"[#2109](https://github.com/adap/flower/pull/2109), "
+"[#2095](https://github.com/adap/flower/pull/2095), "
+"[#2140](https://github.com/adap/flower/pull/2140), "
+"[#2137](https://github.com/adap/flower/pull/2137), "
+"[#2165](https://github.com/adap/flower/pull/2165))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:105
+msgid ""
+"A new testing infrastructure ensures that new changes stay compatible "
+"with existing framework integrations or strategies."
+msgstr ""
+
+#: ../../source/ref-changelog.md:107
+msgid "**Deprecate Python 3.7**"
+msgstr ""
+
+#: ../../source/ref-changelog.md:109
+msgid ""
+"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for"
+" Python 3.7 is now deprecated and will be removed in an upcoming release."
+msgstr ""
+
+#: ../../source/ref-changelog.md:111
+msgid ""
+"**Add new** `FedTrimmedAvg` **strategy** "
+"([#1769](https://github.com/adap/flower/pull/1769), "
+"[#1853](https://github.com/adap/flower/pull/1853))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:113
+msgid ""
+"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, "
+"2018](https://arxiv.org/abs/1803.01498)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:115
+msgid ""
+"**Introduce start_driver** "
+"([#1697](https://github.com/adap/flower/pull/1697))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:117
+msgid ""
+"In addition to `start_server` and using the raw Driver API, there is a "
+"new `start_driver` function that allows for running `start_server` "
+"scripts as a Flower driver with only a single-line code change. Check out"
+" the `mt-pytorch` code example to see a working example using "
+"`start_driver`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:119
+msgid ""
+"**Add parameter aggregation to** `mt-pytorch` **code example** "
+"([#1785](https://github.com/adap/flower/pull/1785))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:121
+msgid ""
+"The `mt-pytorch` example shows how to aggregate parameters when writing a"
+" driver script. The included `driver.py` and `server.py` have been "
+"aligned to demonstrate both the low-level way and the high-level way of "
+"building server-side logic."
+msgstr ""
+
+#: ../../source/ref-changelog.md:123
+msgid ""
+"**Migrate experimental REST API to Starlette** "
+"([2171](https://github.com/adap/flower/pull/2171))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:125
+msgid ""
+"The (experimental) REST API used to be implemented in "
+"[FastAPI](https://fastapi.tiangolo.com/), but it has now been migrated to"
+" use [Starlette](https://www.starlette.io/) directly."
+msgstr ""
+
+#: ../../source/ref-changelog.md:127
+msgid ""
+"Please note: The REST request-response API is still experimental and will"
+" likely change significantly over time."
+msgstr ""
+
+#: ../../source/ref-changelog.md:129
+msgid ""
+"**Introduce experimental gRPC request-response API** "
+"([#1867](https://github.com/adap/flower/pull/1867), "
+"[#1901](https://github.com/adap/flower/pull/1901))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:131
+msgid ""
+"In addition to the existing gRPC API (based on bidirectional streaming) "
+"and the experimental REST API, there is now a new gRPC API that uses a "
+"request-response model to communicate with client nodes."
+msgstr ""
+
+#: ../../source/ref-changelog.md:133
+msgid ""
+"Please note: The gRPC request-response API is still experimental and will"
+" likely change significantly over time."
+msgstr ""
+
+#: ../../source/ref-changelog.md:135
+msgid ""
+"**Replace the experimental** `start_client(rest=True)` **with the new** "
+"`start_client(transport=\"rest\")` "
+"([#1880](https://github.com/adap/flower/pull/1880))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:137
+msgid ""
+"The (experimental) `start_client` argument `rest` was deprecated in "
+"favour of a new argument `transport`. `start_client(transport=\"rest\")` "
+"will yield the same behaviour as `start_client(rest=True)` did before. "
+"All code should migrate to the new argument `transport`. The deprecated "
+"argument `rest` will be removed in a future release."
+msgstr ""
+
+#: ../../source/ref-changelog.md:139
+msgid ""
+"**Add a new gRPC option** "
+"([#2197](https://github.com/adap/flower/pull/2197))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:141
+msgid ""
+"We now start a gRPC server with the `grpc.keepalive_permit_without_calls`"
+" option set to 0 by default. This prevents the clients from sending "
+"keepalive pings when there is no outstanding stream."
+msgstr ""
+
+#: ../../source/ref-changelog.md:143
+msgid ""
+"**Improve example notebooks** "
+"([#2005](https://github.com/adap/flower/pull/2005))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:145
+msgid "There's a new 30min Federated Learning PyTorch tutorial!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:147
+msgid ""
+"**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), "
+"[#1873](https://github.com/adap/flower/pull/1873), "
+"[#1981](https://github.com/adap/flower/pull/1981), "
+"[#1988](https://github.com/adap/flower/pull/1988), "
+"[#1984](https://github.com/adap/flower/pull/1984), "
+"[#1982](https://github.com/adap/flower/pull/1982), "
+"[#2112](https://github.com/adap/flower/pull/2112), "
+"[#2144](https://github.com/adap/flower/pull/2144), "
+"[#2174](https://github.com/adap/flower/pull/2174), "
+"[#2225](https://github.com/adap/flower/pull/2225), "
+"[#2183](https://github.com/adap/flower/pull/2183))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:149
+msgid ""
+"Many examples have received significant updates, including simplified "
+"advanced-tensorflow and advanced-pytorch examples, improved macOS "
+"compatibility of TensorFlow examples, and code examples for simulation. A"
+" major upgrade is that all code examples now have a `requirements.txt` "
+"(in addition to `pyproject.toml`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:151
+msgid ""
+"**General improvements** "
+"([#1872](https://github.com/adap/flower/pull/1872), "
+"[#1866](https://github.com/adap/flower/pull/1866), "
+"[#1884](https://github.com/adap/flower/pull/1884), "
+"[#1837](https://github.com/adap/flower/pull/1837), "
+"[#1477](https://github.com/adap/flower/pull/1477), "
+"[#2171](https://github.com/adap/flower/pull/2171))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:157 ../../source/ref-changelog.md:221
+#: ../../source/ref-changelog.md:279 ../../source/ref-changelog.md:348
+#: ../../source/ref-changelog.md:410
+msgid "None"
+msgstr ""
+
+#: ../../source/ref-changelog.md:159
+msgid "v1.4.0 (2023-04-21)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:165
+msgid ""
+"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
+"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, "
+"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal "
+"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic "
+"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, "
+"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:169
+msgid ""
+"**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and "
+"example)** ([#1694](https://github.com/adap/flower/pull/1694), "
+"[#1709](https://github.com/adap/flower/pull/1709), "
+"[#1715](https://github.com/adap/flower/pull/1715), "
+"[#1717](https://github.com/adap/flower/pull/1717), "
+"[#1763](https://github.com/adap/flower/pull/1763), "
+"[#1795](https://github.com/adap/flower/pull/1795))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:171
+msgid ""
+"XGBoost is a tree-based ensemble machine learning algorithm that uses "
+"gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg`"
+" "
+"[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py),"
+" and a [code "
+"example](https://github.com/adap/flower/tree/main/examples/quickstart_xgboost_horizontal)"
+" that demonstrates the usage of this new strategy in an XGBoost project."
+msgstr ""
+
+#: ../../source/ref-changelog.md:173
+msgid ""
+"**Introduce iOS SDK (preview)** "
+"([#1621](https://github.com/adap/flower/pull/1621), "
+"[#1764](https://github.com/adap/flower/pull/1764))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:175
+msgid ""
+"This is a major update for anyone wanting to implement Federated Learning"
+" on iOS mobile devices. We now have a swift iOS SDK present under "
+"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)"
+" that will facilitate greatly the app creating process. To showcase its "
+"use, the [iOS "
+"example](https://github.com/adap/flower/tree/main/examples/ios) has also "
+"been updated!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:177
+msgid ""
+"**Introduce new \"What is Federated Learning?\" tutorial** "
+"([#1657](https://github.com/adap/flower/pull/1657), "
+"[#1721](https://github.com/adap/flower/pull/1721))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:179
+msgid ""
+"A new [entry-level tutorial](https://flower.dev/docs/framework/tutorial-"
+"what-is-federated-learning.html) in our documentation explains the basics"
+" of Fedetated Learning. It enables anyone who's unfamiliar with Federated"
+" Learning to start their journey with Flower. Forward it to anyone who's "
+"interested in Federated Learning!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:181
+msgid ""
+"**Introduce new Flower Baseline: FedProx MNIST** "
+"([#1513](https://github.com/adap/flower/pull/1513), "
+"[#1680](https://github.com/adap/flower/pull/1680), "
+"[#1681](https://github.com/adap/flower/pull/1681), "
+"[#1679](https://github.com/adap/flower/pull/1679))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:183
+msgid ""
+"This new baseline replicates the MNIST+CNN task from the paper [Federated"
+" Optimization in Heterogeneous Networks (Li et al., "
+"2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy,"
+" which aims at making convergence more robust in heterogenous settings."
+msgstr ""
+
+#: ../../source/ref-changelog.md:185
+msgid ""
+"**Introduce new Flower Baseline: FedAvg FEMNIST** "
+"([#1655](https://github.com/adap/flower/pull/1655))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:187
+msgid ""
+"This new baseline replicates an experiment evaluating the performance of "
+"the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A "
+"Benchmark for Federated Settings (Caldas et al., "
+"2018)](https://arxiv.org/abs/1812.01097)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:189
+msgid ""
+"**Introduce (experimental) REST API** "
+"([#1594](https://github.com/adap/flower/pull/1594), "
+"[#1690](https://github.com/adap/flower/pull/1690), "
+"[#1695](https://github.com/adap/flower/pull/1695), "
+"[#1712](https://github.com/adap/flower/pull/1712), "
+"[#1802](https://github.com/adap/flower/pull/1802), "
+"[#1770](https://github.com/adap/flower/pull/1770), "
+"[#1733](https://github.com/adap/flower/pull/1733))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:191
+msgid ""
+"A new REST API has been introduced as an alternative to the gRPC-based "
+"communication stack. In this initial version, the REST API only supports "
+"anonymous clients."
+msgstr ""
+
+#: ../../source/ref-changelog.md:193
+msgid ""
+"Please note: The REST API is still experimental and will likely change "
+"significantly over time."
+msgstr ""
+
+#: ../../source/ref-changelog.md:195
+msgid ""
+"**Improve the (experimental) Driver API** "
+"([#1663](https://github.com/adap/flower/pull/1663), "
+"[#1666](https://github.com/adap/flower/pull/1666), "
+"[#1667](https://github.com/adap/flower/pull/1667), "
+"[#1664](https://github.com/adap/flower/pull/1664), "
+"[#1675](https://github.com/adap/flower/pull/1675), "
+"[#1676](https://github.com/adap/flower/pull/1676), "
+"[#1693](https://github.com/adap/flower/pull/1693), "
+"[#1662](https://github.com/adap/flower/pull/1662), "
+"[#1794](https://github.com/adap/flower/pull/1794))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:197
+msgid ""
+"The Driver API is still an experimental feature, but this release "
+"introduces some major upgrades. One of the main improvements is the "
+"introduction of an SQLite database to store server state on disk (instead"
+" of in-memory). Another improvement is that tasks (instructions or "
+"results) that have been delivered will now be deleted. This greatly "
+"improves the memory efficiency of a long-running Flower server."
+msgstr ""
+
+#: ../../source/ref-changelog.md:199
+msgid ""
+"**Fix spilling issues related to Ray during simulations** "
+"([#1698](https://github.com/adap/flower/pull/1698))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:201
+msgid ""
+"While running long simulations, `ray` was sometimes spilling huge amounts"
+" of data that would make the training unable to continue. This is now "
+"fixed! 🎉"
+msgstr ""
+
+#: ../../source/ref-changelog.md:203
+msgid ""
+"**Add new example using** `TabNet` **and Flower** "
+"([#1725](https://github.com/adap/flower/pull/1725))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:205
+msgid ""
+"TabNet is a powerful and flexible framework for training machine learning"
+" models on tabular data. We now have a federated example using Flower: "
+"[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:207
+msgid ""
+"**Add new how-to guide for monitoring simulations** "
+"([#1649](https://github.com/adap/flower/pull/1649))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:209
+msgid ""
+"We now have a documentation guide to help users monitor their performance"
+" during simulations."
+msgstr ""
+
+#: ../../source/ref-changelog.md:211
+msgid ""
+"**Add training metrics to** `History` **object during simulations** "
+"([#1696](https://github.com/adap/flower/pull/1696))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:213
+msgid ""
+"The `fit_metrics_aggregation_fn` can be used to aggregate training "
+"metrics, but previous releases did not save the results in the `History` "
+"object. This is now the case!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:215
+msgid ""
+"**General improvements** "
+"([#1659](https://github.com/adap/flower/pull/1659), "
+"[#1646](https://github.com/adap/flower/pull/1646), "
+"[#1647](https://github.com/adap/flower/pull/1647), "
+"[#1471](https://github.com/adap/flower/pull/1471), "
+"[#1648](https://github.com/adap/flower/pull/1648), "
+"[#1651](https://github.com/adap/flower/pull/1651), "
+"[#1652](https://github.com/adap/flower/pull/1652), "
+"[#1653](https://github.com/adap/flower/pull/1653), "
+"[#1659](https://github.com/adap/flower/pull/1659), "
+"[#1665](https://github.com/adap/flower/pull/1665), "
+"[#1670](https://github.com/adap/flower/pull/1670), "
+"[#1672](https://github.com/adap/flower/pull/1672), "
+"[#1677](https://github.com/adap/flower/pull/1677), "
+"[#1684](https://github.com/adap/flower/pull/1684), "
+"[#1683](https://github.com/adap/flower/pull/1683), "
+"[#1686](https://github.com/adap/flower/pull/1686), "
+"[#1682](https://github.com/adap/flower/pull/1682), "
+"[#1685](https://github.com/adap/flower/pull/1685), "
+"[#1692](https://github.com/adap/flower/pull/1692), "
+"[#1705](https://github.com/adap/flower/pull/1705), "
+"[#1708](https://github.com/adap/flower/pull/1708), "
+"[#1711](https://github.com/adap/flower/pull/1711), "
+"[#1713](https://github.com/adap/flower/pull/1713), "
+"[#1714](https://github.com/adap/flower/pull/1714), "
+"[#1718](https://github.com/adap/flower/pull/1718), "
+"[#1716](https://github.com/adap/flower/pull/1716), "
+"[#1723](https://github.com/adap/flower/pull/1723), "
+"[#1735](https://github.com/adap/flower/pull/1735), "
+"[#1678](https://github.com/adap/flower/pull/1678), "
+"[#1750](https://github.com/adap/flower/pull/1750), "
+"[#1753](https://github.com/adap/flower/pull/1753), "
+"[#1736](https://github.com/adap/flower/pull/1736), "
+"[#1766](https://github.com/adap/flower/pull/1766), "
+"[#1760](https://github.com/adap/flower/pull/1760), "
+"[#1775](https://github.com/adap/flower/pull/1775), "
+"[#1776](https://github.com/adap/flower/pull/1776), "
+"[#1777](https://github.com/adap/flower/pull/1777), "
+"[#1779](https://github.com/adap/flower/pull/1779), "
+"[#1784](https://github.com/adap/flower/pull/1784), "
+"[#1773](https://github.com/adap/flower/pull/1773), "
+"[#1755](https://github.com/adap/flower/pull/1755), "
+"[#1789](https://github.com/adap/flower/pull/1789), "
+"[#1788](https://github.com/adap/flower/pull/1788), "
+"[#1798](https://github.com/adap/flower/pull/1798), "
+"[#1799](https://github.com/adap/flower/pull/1799), "
+"[#1739](https://github.com/adap/flower/pull/1739), "
+"[#1800](https://github.com/adap/flower/pull/1800), "
+"[#1804](https://github.com/adap/flower/pull/1804), "
+"[#1805](https://github.com/adap/flower/pull/1805))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:223
+msgid "v1.3.0 (2023-02-06)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:229
+msgid ""
+"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
+"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:233
+msgid ""
+"**Add support for** `workload_id` **and** `group_id` **in Driver API** "
+"([#1595](https://github.com/adap/flower/pull/1595))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:235
+msgid ""
+"The (experimental) Driver API now supports a `workload_id` that can be "
+"used to identify which workload a task belongs to. It also supports a new"
+" `group_id` that can be used, for example, to indicate the current "
+"training round. Both the `workload_id` and `group_id` enable client nodes"
+" to decide whether they want to handle a task or not."
+msgstr ""
+
+#: ../../source/ref-changelog.md:237
+msgid ""
+"**Make Driver API and Fleet API address configurable** "
+"([#1637](https://github.com/adap/flower/pull/1637))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:239
+msgid ""
+"The (experimental) long-running Flower server (Driver API and Fleet API) "
+"can now configure the server address of both Driver API (via `--driver-"
+"api-address`) and Fleet API (via `--fleet-api-address`) when starting:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:241
+msgid ""
+"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address "
+"\"0.0.0.0:8086\"`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:243
+msgid "Both IPv4 and IPv6 addresses are supported."
+msgstr ""
+
+#: ../../source/ref-changelog.md:245
+msgid ""
+"**Add new example of Federated Learning using fastai and Flower** "
+"([#1598](https://github.com/adap/flower/pull/1598))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:247
+msgid ""
+"A new code example (`quickstart_fastai`) demonstrates federated learning "
+"with [fastai](https://www.fast.ai/) and Flower. You can find it here: "
+"[quickstart_fastai](https://github.com/adap/flower/tree/main/examples/quickstart_fastai)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:249
+msgid ""
+"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest"
+" versions of Android** "
+"([#1603](https://github.com/adap/flower/pull/1603))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:251
+msgid ""
+"The Android code example has received a substantial update: the project "
+"is compatible with Flower 1.0 (and later), the UI received a full "
+"refresh, and the project is updated to be compatible with newer Android "
+"tooling."
+msgstr ""
+
+#: ../../source/ref-changelog.md:253
+msgid ""
+"**Add new `FedProx` strategy** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:255
+msgid ""
+"This "
+"[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)"
+" is almost identical to "
+"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py),"
+" but helps users replicate what is described in this "
+"[paper](https://arxiv.org/abs/1812.06127). It essentially adds a "
+"parameter called `proximal_mu` to regularize the local models with "
+"respect to the global models."
+msgstr ""
+
+#: ../../source/ref-changelog.md:257
+msgid ""
+"**Add new metrics to telemetry events** "
+"([#1640](https://github.com/adap/flower/pull/1640))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:259
+msgid ""
+"An updated event structure allows, for example, the clustering of events "
+"within the same workload."
+msgstr ""
+
+#: ../../source/ref-changelog.md:261
+msgid ""
+"**Add new custom strategy tutorial section** "
+"[#1623](https://github.com/adap/flower/pull/1623)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:263
+msgid ""
+"The Flower tutorial now has a new section that covers implementing a "
+"custom strategy from scratch: [Open in "
+"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source"
+"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:265
+msgid ""
+"**Add new custom serialization tutorial section** "
+"([#1622](https://github.com/adap/flower/pull/1622))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:267
+msgid ""
+"The Flower tutorial now has a new section that covers custom "
+"serialization: [Open in "
+"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source"
+"/tutorial-customize-the-client-pytorch.ipynb)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:269
+msgid ""
+"**General improvements** "
+"([#1638](https://github.com/adap/flower/pull/1638), "
+"[#1634](https://github.com/adap/flower/pull/1634), "
+"[#1636](https://github.com/adap/flower/pull/1636), "
+"[#1635](https://github.com/adap/flower/pull/1635), "
+"[#1633](https://github.com/adap/flower/pull/1633), "
+"[#1632](https://github.com/adap/flower/pull/1632), "
+"[#1631](https://github.com/adap/flower/pull/1631), "
+"[#1630](https://github.com/adap/flower/pull/1630), "
+"[#1627](https://github.com/adap/flower/pull/1627), "
+"[#1593](https://github.com/adap/flower/pull/1593), "
+"[#1616](https://github.com/adap/flower/pull/1616), "
+"[#1615](https://github.com/adap/flower/pull/1615), "
+"[#1607](https://github.com/adap/flower/pull/1607), "
+"[#1609](https://github.com/adap/flower/pull/1609), "
+"[#1608](https://github.com/adap/flower/pull/1608), "
+"[#1603](https://github.com/adap/flower/pull/1603), "
+"[#1590](https://github.com/adap/flower/pull/1590), "
+"[#1580](https://github.com/adap/flower/pull/1580), "
+"[#1599](https://github.com/adap/flower/pull/1599), "
+"[#1600](https://github.com/adap/flower/pull/1600), "
+"[#1601](https://github.com/adap/flower/pull/1601), "
+"[#1597](https://github.com/adap/flower/pull/1597), "
+"[#1595](https://github.com/adap/flower/pull/1595), "
+"[#1591](https://github.com/adap/flower/pull/1591), "
+"[#1588](https://github.com/adap/flower/pull/1588), "
+"[#1589](https://github.com/adap/flower/pull/1589), "
+"[#1587](https://github.com/adap/flower/pull/1587), "
+"[#1573](https://github.com/adap/flower/pull/1573), "
+"[#1581](https://github.com/adap/flower/pull/1581), "
+"[#1578](https://github.com/adap/flower/pull/1578), "
+"[#1574](https://github.com/adap/flower/pull/1574), "
+"[#1572](https://github.com/adap/flower/pull/1572), "
+"[#1586](https://github.com/adap/flower/pull/1586))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:273
+msgid ""
+"**Updated documentation** "
+"([#1629](https://github.com/adap/flower/pull/1629), "
+"[#1628](https://github.com/adap/flower/pull/1628), "
+"[#1620](https://github.com/adap/flower/pull/1620), "
+"[#1618](https://github.com/adap/flower/pull/1618), "
+"[#1617](https://github.com/adap/flower/pull/1617), "
+"[#1613](https://github.com/adap/flower/pull/1613), "
+"[#1614](https://github.com/adap/flower/pull/1614))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:275 ../../source/ref-changelog.md:342
+msgid ""
+"As usual, the documentation has improved quite a bit. It is another step "
+"in our effort to make the Flower documentation the best documentation of "
+"any project. Stay tuned and as always, feel free to provide feedback!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:281
+msgid "v1.2.0 (2023-01-13)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:287
+msgid ""
+"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L."
+" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:291
+msgid ""
+"**Introduce new Flower Baseline: FedAvg MNIST** "
+"([#1497](https://github.com/adap/flower/pull/1497), "
+"[#1552](https://github.com/adap/flower/pull/1552))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:293
+msgid ""
+"Over the coming weeks, we will be releasing a number of new reference "
+"implementations useful especially to FL newcomers. They will typically "
+"revisit well known papers from the literature, and be suitable for "
+"integration in your own application or for experimentation, in order to "
+"deepen your knowledge of FL in general. Today's release is the first in "
+"this series. [Read more.](https://flower.dev/blog/2023-01-12-fl-starter-"
+"pack-fedavg-mnist-cnn/)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:295
+msgid ""
+"**Improve GPU support in simulations** "
+"([#1555](https://github.com/adap/flower/pull/1555))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:297
+msgid ""
+"The Ray-based Virtual Client Engine (`start_simulation`) has been updated"
+" to improve GPU support. The update includes some of the hard-earned "
+"lessons from scaling simulations in GPU cluster environments. New "
+"defaults make running GPU-based simulations substantially more robust."
+msgstr ""
+
+#: ../../source/ref-changelog.md:299
+msgid ""
+"**Improve GPU support in Jupyter Notebook tutorials** "
+"([#1527](https://github.com/adap/flower/pull/1527), "
+"[#1558](https://github.com/adap/flower/pull/1558))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:301
+msgid ""
+"Some users reported that Jupyter Notebooks have not always been easy to "
+"use on GPU instances. We listened and made improvements to all of our "
+"Jupyter notebooks! Check out the updated notebooks here:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:303
+msgid ""
+"[An Introduction to Federated Learning](https://flower.dev/docs/framework"
+"/tutorial-get-started-with-flower-pytorch.html)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:304
+msgid ""
+"[Strategies in Federated Learning](https://flower.dev/docs/framework"
+"/tutorial-use-a-federated-learning-strategy-pytorch.html)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:305
+msgid ""
+"[Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a"
+"-strategy-from-scratch-pytorch.html)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:306
+msgid ""
+"[Client and NumPyClient](https://flower.dev/docs/framework/tutorial-"
+"customize-the-client-pytorch.html)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:308
+msgid ""
+"**Introduce optional telemetry** "
+"([#1533](https://github.com/adap/flower/pull/1533), "
+"[#1544](https://github.com/adap/flower/pull/1544), "
+"[#1584](https://github.com/adap/flower/pull/1584))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:310
+msgid ""
+"After a [request for "
+"feedback](https://github.com/adap/flower/issues/1534) from the community,"
+" the Flower open-source project introduces optional collection of "
+"*anonymous* usage metrics to make well-informed decisions to improve "
+"Flower. Doing this enables the Flower team to understand how Flower is "
+"used and what challenges users might face."
+msgstr ""
+
+#: ../../source/ref-changelog.md:312
+msgid ""
+"**Flower is a friendly framework for collaborative AI and data science.**"
+" Staying true to this statement, Flower makes it easy to disable "
+"telemetry for users who do not want to share anonymous usage metrics. "
+"[Read more.](https://flower.dev/docs/telemetry.html)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:314
+msgid ""
+"**Introduce (experimental) Driver API** "
+"([#1520](https://github.com/adap/flower/pull/1520), "
+"[#1525](https://github.com/adap/flower/pull/1525), "
+"[#1545](https://github.com/adap/flower/pull/1545), "
+"[#1546](https://github.com/adap/flower/pull/1546), "
+"[#1550](https://github.com/adap/flower/pull/1550), "
+"[#1551](https://github.com/adap/flower/pull/1551), "
+"[#1567](https://github.com/adap/flower/pull/1567))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:316
+msgid ""
+"Flower now has a new (experimental) Driver API which will enable fully "
+"programmable, async, and multi-tenant Federated Learning and Federated "
+"Analytics applications. Phew, that's a lot! Going forward, the Driver API"
+" will be the abstraction that many upcoming features will be built on - "
+"and you can start building those things now, too."
+msgstr ""
+
+#: ../../source/ref-changelog.md:318
+msgid ""
+"The Driver API also enables a new execution mode in which the server runs"
+" indefinitely. Multiple individual workloads can run concurrently and "
+"start and stop their execution independent of the server. This is "
+"especially useful for users who want to deploy Flower in production."
+msgstr ""
+
+#: ../../source/ref-changelog.md:320
+msgid ""
+"To learn more, check out the `mt-pytorch` code example. We look forward "
+"to you feedback!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:322
+msgid ""
+"Please note: *The Driver API is still experimental and will likely change"
+" significantly over time.*"
+msgstr ""
+
+#: ../../source/ref-changelog.md:324
+msgid ""
+"**Add new Federated Analytics with Pandas example** "
+"([#1469](https://github.com/adap/flower/pull/1469), "
+"[#1535](https://github.com/adap/flower/pull/1535))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:326
+msgid ""
+"A new code example (`quickstart_pandas`) demonstrates federated analytics"
+" with Pandas and Flower. You can find it here: "
+"[quickstart_pandas](https://github.com/adap/flower/tree/main/examples/quickstart_pandas)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:328
+msgid ""
+"**Add new strategies: Krum and MultiKrum** "
+"([#1481](https://github.com/adap/flower/pull/1481))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:330
+msgid ""
+"Edoardo, a computer science student at the Sapienza University of Rome, "
+"contributed a new `Krum` strategy that enables users to easily use Krum "
+"and MultiKrum in their workloads."
+msgstr ""
+
+#: ../../source/ref-changelog.md:332
+msgid ""
+"**Update C++ example to be compatible with Flower v1.2.0** "
+"([#1495](https://github.com/adap/flower/pull/1495))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:334
+msgid ""
+"The C++ code example has received a substantial update to make it "
+"compatible with the latest version of Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:336
+msgid ""
+"**General improvements** "
+"([#1491](https://github.com/adap/flower/pull/1491), "
+"[#1504](https://github.com/adap/flower/pull/1504), "
+"[#1506](https://github.com/adap/flower/pull/1506), "
+"[#1514](https://github.com/adap/flower/pull/1514), "
+"[#1522](https://github.com/adap/flower/pull/1522), "
+"[#1523](https://github.com/adap/flower/pull/1523), "
+"[#1526](https://github.com/adap/flower/pull/1526), "
+"[#1528](https://github.com/adap/flower/pull/1528), "
+"[#1547](https://github.com/adap/flower/pull/1547), "
+"[#1549](https://github.com/adap/flower/pull/1549), "
+"[#1560](https://github.com/adap/flower/pull/1560), "
+"[#1564](https://github.com/adap/flower/pull/1564), "
+"[#1566](https://github.com/adap/flower/pull/1566))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:340
+msgid ""
+"**Updated documentation** "
+"([#1494](https://github.com/adap/flower/pull/1494), "
+"[#1496](https://github.com/adap/flower/pull/1496), "
+"[#1500](https://github.com/adap/flower/pull/1500), "
+"[#1503](https://github.com/adap/flower/pull/1503), "
+"[#1505](https://github.com/adap/flower/pull/1505), "
+"[#1524](https://github.com/adap/flower/pull/1524), "
+"[#1518](https://github.com/adap/flower/pull/1518), "
+"[#1519](https://github.com/adap/flower/pull/1519), "
+"[#1515](https://github.com/adap/flower/pull/1515))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:344
+msgid ""
+"One highlight is the new [first time contributor "
+"guide](https://flower.dev/docs/first-time-contributors.html): if you've "
+"never contributed on GitHub before, this is the perfect place to start!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:350
+msgid "v1.1.0 (2022-10-31)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:354
+msgid ""
+"We would like to give our **special thanks** to all the contributors who "
+"made the new version of Flower possible (in `git shortlog` order):"
+msgstr ""
+
+#: ../../source/ref-changelog.md:356
+msgid ""
+"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan "
+"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp "
+"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, "
+"`danielnugraha`, `edogab33`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:360
+msgid ""
+"**Introduce Differential Privacy wrappers (preview)** "
+"([#1357](https://github.com/adap/flower/pull/1357), "
+"[#1460](https://github.com/adap/flower/pull/1460))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:362
+msgid ""
+"The first (experimental) preview of pluggable Differential Privacy "
+"wrappers enables easy configuration and usage of differential privacy "
+"(DP). The pluggable DP wrappers enable framework-agnostic **and** "
+"strategy-agnostic usage of both client-side DP and server-side DP. Head "
+"over to the Flower docs, a new explainer goes into more detail."
+msgstr ""
+
+#: ../../source/ref-changelog.md:364
+msgid ""
+"**New iOS CoreML code example** "
+"([#1289](https://github.com/adap/flower/pull/1289))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:366
+msgid ""
+"Flower goes iOS! A massive new code example shows how Flower clients can "
+"be built for iOS. The code example contains both Flower iOS SDK "
+"components that can be used for many tasks, and one task example running "
+"on CoreML."
+msgstr ""
+
+#: ../../source/ref-changelog.md:368
+msgid ""
+"**New FedMedian strategy** "
+"([#1461](https://github.com/adap/flower/pull/1461))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:370
+msgid ""
+"The new `FedMedian` strategy implements Federated Median (FedMedian) by "
+"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:372
+msgid ""
+"**Log** `Client` **exceptions in Virtual Client Engine** "
+"([#1493](https://github.com/adap/flower/pull/1493))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:374
+msgid ""
+"All `Client` exceptions happening in the VCE are now logged by default "
+"and not just exposed to the configured `Strategy` (via the `failures` "
+"argument)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:376
+msgid ""
+"**Improve Virtual Client Engine internals** "
+"([#1401](https://github.com/adap/flower/pull/1401), "
+"[#1453](https://github.com/adap/flower/pull/1453))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:378
+msgid ""
+"Some internals of the Virtual Client Engine have been revamped. The VCE "
+"now uses Ray 2.0 under the hood, the value type of the `client_resources`"
+" dictionary changed to `float` to allow fractions of resources to be "
+"allocated."
+msgstr ""
+
+#: ../../source/ref-changelog.md:380
+msgid ""
+"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual "
+"Client Engine**"
+msgstr ""
+
+#: ../../source/ref-changelog.md:382
+msgid ""
+"The Virtual Client Engine now has full support for optional `Client` (and"
+" `NumPyClient`) methods."
+msgstr ""
+
+#: ../../source/ref-changelog.md:384
+msgid ""
+"**Provide type information to packages using** `flwr` "
+"([#1377](https://github.com/adap/flower/pull/1377))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:386
+msgid ""
+"The package `flwr` is now bundled with a `py.typed` file indicating that "
+"the package is typed. This enables typing support for projects or "
+"packages that use `flwr` by enabling them to improve their code using "
+"static type checkers like `mypy`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:388
+msgid ""
+"**Updated code example** "
+"([#1344](https://github.com/adap/flower/pull/1344), "
+"[#1347](https://github.com/adap/flower/pull/1347))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:390
+msgid ""
+"The code examples covering scikit-learn and PyTorch Lightning have been "
+"updated to work with the latest version of Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:392
+msgid ""
+"**Updated documentation** "
+"([#1355](https://github.com/adap/flower/pull/1355), "
+"[#1558](https://github.com/adap/flower/pull/1558), "
+"[#1379](https://github.com/adap/flower/pull/1379), "
+"[#1380](https://github.com/adap/flower/pull/1380), "
+"[#1381](https://github.com/adap/flower/pull/1381), "
+"[#1332](https://github.com/adap/flower/pull/1332), "
+"[#1391](https://github.com/adap/flower/pull/1391), "
+"[#1403](https://github.com/adap/flower/pull/1403), "
+"[#1364](https://github.com/adap/flower/pull/1364), "
+"[#1409](https://github.com/adap/flower/pull/1409), "
+"[#1419](https://github.com/adap/flower/pull/1419), "
+"[#1444](https://github.com/adap/flower/pull/1444), "
+"[#1448](https://github.com/adap/flower/pull/1448), "
+"[#1417](https://github.com/adap/flower/pull/1417), "
+"[#1449](https://github.com/adap/flower/pull/1449), "
+"[#1465](https://github.com/adap/flower/pull/1465), "
+"[#1467](https://github.com/adap/flower/pull/1467))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:394
+msgid ""
+"There have been so many documentation updates that it doesn't even make "
+"sense to list them individually."
+msgstr ""
+
+#: ../../source/ref-changelog.md:396
+msgid ""
+"**Restructured documentation** "
+"([#1387](https://github.com/adap/flower/pull/1387))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:398
+msgid ""
+"The documentation has been restructured to make it easier to navigate. "
+"This is just the first step in a larger effort to make the Flower "
+"documentation the best documentation of any project ever. Stay tuned!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:400
+msgid ""
+"**Open in Colab button** "
+"([#1389](https://github.com/adap/flower/pull/1389))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:402
+msgid ""
+"The four parts of the Flower Federated Learning Tutorial now come with a "
+"new `Open in Colab` button. No need to install anything on your local "
+"machine, you can now use and learn about Flower in your browser, it's "
+"only a single click away."
+msgstr ""
+
+#: ../../source/ref-changelog.md:404
+msgid ""
+"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468),"
+" [#1470](https://github.com/adap/flower/pull/1470), "
+"[#1472](https://github.com/adap/flower/pull/1472), "
+"[#1473](https://github.com/adap/flower/pull/1473), "
+"[#1474](https://github.com/adap/flower/pull/1474), "
+"[#1475](https://github.com/adap/flower/pull/1475))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:406
+msgid ""
+"The Flower Federated Learning Tutorial has two brand-new parts covering "
+"custom strategies (still WIP) and the distinction between `Client` and "
+"`NumPyClient`. The existing parts one and two have also been improved "
+"(many small changes and fixes)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:412
+msgid "v1.0.0 (2022-07-28)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:414
+msgid "Highlights"
+msgstr ""
+
+#: ../../source/ref-changelog.md:416
+msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:417
+msgid "All `Client`/`NumPyClient` methods are now optional"
+msgstr ""
+
+#: ../../source/ref-changelog.md:418
+msgid "Configurable `get_parameters`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:419
+msgid ""
+"Tons of small API cleanups resulting in a more coherent developer "
+"experience"
+msgstr ""
+
+#: ../../source/ref-changelog.md:423
+msgid ""
+"We would like to give our **special thanks** to all the contributors who "
+"made Flower 1.0 possible (in reverse [GitHub "
+"Contributors](https://github.com/adap/flower/graphs/contributors) order):"
+msgstr ""
+
+#: ../../source/ref-changelog.md:425
+msgid ""
+"[@rtaiello](https://github.com/rtaiello), "
+"[@g-pichler](https://github.com/g-pichler), [@rob-"
+"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com"
+"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), "
+"[@nfnt](https://github.com/nfnt), "
+"[@tatiana-s](https://github.com/tatiana-s), "
+"[@TParcollet](https://github.com/TParcollet), "
+"[@vballoli](https://github.com/vballoli), "
+"[@negedng](https://github.com/negedng), "
+"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), "
+"[@hei411](https://github.com/hei411), "
+"[@SebastianSpeitel](https://github.com/SebastianSpeitel), "
+"[@AmitChaulwar](https://github.com/AmitChaulwar), "
+"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com"
+"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), "
+"[@lbhm](https://github.com/lbhm), "
+"[@sishtiaq](https://github.com/sishtiaq), "
+"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com"
+"/Jueun-Park), [@architjen](https://github.com/architjen), "
+"[@PratikGarai](https://github.com/PratikGarai), "
+"[@mrinaald](https://github.com/mrinaald), "
+"[@zliel](https://github.com/zliel), "
+"[@MeiruiJiang](https://github.com/MeiruiJiang), "
+"[@sandracl72](https://github.com/sandracl72), "
+"[@gubertoli](https://github.com/gubertoli), "
+"[@Vingt100](https://github.com/Vingt100), "
+"[@MakGulati](https://github.com/MakGulati), "
+"[@cozek](https://github.com/cozek), "
+"[@jafermarq](https://github.com/jafermarq), "
+"[@sisco0](https://github.com/sisco0), "
+"[@akhilmathurs](https://github.com/akhilmathurs), "
+"[@CanTuerk](https://github.com/CanTuerk), "
+"[@mariaboerner1987](https://github.com/mariaboerner1987), "
+"[@pedropgusmao](https://github.com/pedropgusmao), "
+"[@tanertopal](https://github.com/tanertopal), "
+"[@danieljanes](https://github.com/danieljanes)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:429
+msgid ""
+"**All arguments must be passed as keyword arguments** "
+"([#1338](https://github.com/adap/flower/pull/1338))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:431
+msgid ""
+"Pass all arguments as keyword arguments, positional arguments are not "
+"longer supported. Code that uses positional arguments (e.g., "
+"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword "
+"for each positional argument (e.g., "
+"`start_client(server_address=\"127.0.0.1:8080\", "
+"client=FlowerClient())`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:433
+msgid ""
+"**Introduce configuration object** `ServerConfig` **in** `start_server` "
+"**and** `start_simulation` "
+"([#1317](https://github.com/adap/flower/pull/1317))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:435
+msgid ""
+"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": "
+"600.0}`, `start_server` and `start_simulation` now expect a configuration"
+" object of type `flwr.server.ServerConfig`. `ServerConfig` takes the same"
+" arguments that as the previous config dict, but it makes writing type-"
+"safe code easier and the default parameters values more transparent."
+msgstr ""
+
+#: ../../source/ref-changelog.md:437
+msgid ""
+"**Rename built-in strategy parameters for clarity** "
+"([#1334](https://github.com/adap/flower/pull/1334))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:439
+msgid ""
+"The following built-in strategy parameters were renamed to improve "
+"readability and consistency with other API's:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:441
+msgid "`fraction_eval` --> `fraction_evaluate`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:442
+msgid "`min_eval_clients` --> `min_evaluate_clients`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:443
+msgid "`eval_fn` --> `evaluate_fn`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:445
+msgid ""
+"**Update default arguments of built-in strategies** "
+"([#1278](https://github.com/adap/flower/pull/1278))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:447
+msgid ""
+"All built-in strategies now use `fraction_fit=1.0` and "
+"`fraction_evaluate=1.0`, which means they select *all* currently "
+"available clients for training and evaluation. Projects that relied on "
+"the previous default values can get the previous behaviour by "
+"initializing the strategy in the following way:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:449
+msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:451
+msgid ""
+"**Add** `server_round` **to** `Strategy.evaluate` "
+"([#1334](https://github.com/adap/flower/pull/1334))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:453
+msgid ""
+"The `Strategy` method `evaluate` now receives the current round of "
+"federated learning/evaluation as the first parameter."
+msgstr ""
+
+#: ../../source/ref-changelog.md:455
+msgid ""
+"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` "
+"([#1334](https://github.com/adap/flower/pull/1334))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:457
+msgid ""
+"The `evaluate_fn` passed to built-in strategies like `FedAvg` now takes "
+"three parameters: (1) The current round of federated learning/evaluation "
+"(`server_round`), (2) the model parameters to evaluate (`parameters`), "
+"and (3) a config dictionary (`config`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:459
+msgid ""
+"**Rename** `rnd` **to** `server_round` "
+"([#1321](https://github.com/adap/flower/pull/1321))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:461
+msgid ""
+"Several Flower methods and functions (`evaluate_fn`, `configure_fit`, "
+"`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the "
+"current round of federated learning/evaluation as their first parameter. "
+"To improve reaability and avoid confusion with *random*, this parameter "
+"has been renamed from `rnd` to `server_round`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:463
+msgid ""
+"**Move** `flwr.dataset` **to** `flwr_baselines` "
+"([#1273](https://github.com/adap/flower/pull/1273))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:465
+msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines."
+msgstr ""
+
+#: ../../source/ref-changelog.md:467
+msgid ""
+"**Remove experimental strategies** "
+"([#1280](https://github.com/adap/flower/pull/1280))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:469
+msgid ""
+"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, "
+"`FedFSv1`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:471
+msgid ""
+"**Rename** `Weights` **to** `NDArrays` "
+"([#1258](https://github.com/adap/flower/pull/1258), "
+"[#1259](https://github.com/adap/flower/pull/1259))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:473
+msgid ""
+"`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better "
+"capture what this type is all about."
+msgstr ""
+
+#: ../../source/ref-changelog.md:475
+msgid ""
+"**Remove antiquated** `force_final_distributed_eval` **from** "
+"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), "
+"[#1259](https://github.com/adap/flower/pull/1259))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:477
+msgid ""
+"The `start_server` parameter `force_final_distributed_eval` has long been"
+" a historic artefact, in this release it is finally gone for good."
+msgstr ""
+
+#: ../../source/ref-changelog.md:479
+msgid ""
+"**Make** `get_parameters` **configurable** "
+"([#1242](https://github.com/adap/flower/pull/1242))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:481
+msgid ""
+"The `get_parameters` method now accepts a configuration dictionary, just "
+"like `get_properties`, `fit`, and `evaluate`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:483
+msgid ""
+"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` "
+"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:485
+msgid ""
+"The `start_simulation` function now accepts a configuration dictionary "
+"`config` instead of the `num_rounds` integer. This improves the "
+"consistency between `start_simulation` and `start_server` and makes "
+"transitioning between the two easier."
+msgstr ""
+
+#: ../../source/ref-changelog.md:489
+msgid ""
+"**Support Python 3.10** "
+"([#1320](https://github.com/adap/flower/pull/1320))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:491
+msgid ""
+"The previous Flower release introduced experimental support for Python "
+"3.10, this release declares Python 3.10 support as stable."
+msgstr ""
+
+#: ../../source/ref-changelog.md:493
+msgid ""
+"**Make all** `Client` **and** `NumPyClient` **methods optional** "
+"([#1260](https://github.com/adap/flower/pull/1260), "
+"[#1277](https://github.com/adap/flower/pull/1277))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:495
+msgid ""
+"The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, "
+"`fit`, and `evaluate` are all optional. This enables writing clients that"
+" implement, for example, only `fit`, but no other method. No need to "
+"implement `evaluate` when using centralized evaluation!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:497
+msgid ""
+"**Enable passing a** `Server` **instance to** `start_simulation` "
+"([#1281](https://github.com/adap/flower/pull/1281))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:499
+msgid ""
+"Similar to `start_server`, `start_simulation` now accepts a full `Server`"
+" instance. This enables users to heavily customize the execution of "
+"eperiments and opens the door to running, for example, async FL using the"
+" Virtual Client Engine."
+msgstr ""
+
+#: ../../source/ref-changelog.md:501
+msgid ""
+"**Update code examples** "
+"([#1291](https://github.com/adap/flower/pull/1291), "
+"[#1286](https://github.com/adap/flower/pull/1286), "
+"[#1282](https://github.com/adap/flower/pull/1282))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:503
+msgid ""
+"Many code examples received small or even large maintenance updates, "
+"among them are"
+msgstr ""
+
+#: ../../source/ref-changelog.md:505
+msgid "`scikit-learn`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:506
+msgid "`simulation_pytorch`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:507
+msgid "`quickstart_pytorch`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:508
+msgid "`quickstart_simulation`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:509
+msgid "`quickstart_tensorflow`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:510
+msgid "`advanced_tensorflow`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:512
+msgid ""
+"**Remove the obsolete simulation example** "
+"([#1328](https://github.com/adap/flower/pull/1328))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:514
+msgid ""
+"Removes the obsolete `simulation` example and renames "
+"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the "
+"naming of `simulation_pytorch`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:516
+msgid ""
+"**Update documentation** "
+"([#1223](https://github.com/adap/flower/pull/1223), "
+"[#1209](https://github.com/adap/flower/pull/1209), "
+"[#1251](https://github.com/adap/flower/pull/1251), "
+"[#1257](https://github.com/adap/flower/pull/1257), "
+"[#1267](https://github.com/adap/flower/pull/1267), "
+"[#1268](https://github.com/adap/flower/pull/1268), "
+"[#1300](https://github.com/adap/flower/pull/1300), "
+"[#1304](https://github.com/adap/flower/pull/1304), "
+"[#1305](https://github.com/adap/flower/pull/1305), "
+"[#1307](https://github.com/adap/flower/pull/1307))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:518
+msgid ""
+"One substantial documentation update fixes multiple smaller rendering "
+"issues, makes titles more succinct to improve navigation, removes a "
+"deprecated library, updates documentation dependencies, includes the "
+"`flwr.common` module in the API reference, includes support for markdown-"
+"based documentation, migrates the changelog from `.rst` to `.md`, and "
+"fixes a number of smaller details!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:520 ../../source/ref-changelog.md:575
+#: ../../source/ref-changelog.md:644 ../../source/ref-changelog.md:683
+msgid "**Minor updates**"
+msgstr ""
+
+#: ../../source/ref-changelog.md:522
+msgid ""
+"Add round number to fit and evaluate log messages "
+"([#1266](https://github.com/adap/flower/pull/1266))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:523
+msgid ""
+"Add secure gRPC connection to the `advanced_tensorflow` code example "
+"([#847](https://github.com/adap/flower/pull/847))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:524
+msgid ""
+"Update developer tooling "
+"([#1231](https://github.com/adap/flower/pull/1231), "
+"[#1276](https://github.com/adap/flower/pull/1276), "
+"[#1301](https://github.com/adap/flower/pull/1301), "
+"[#1310](https://github.com/adap/flower/pull/1310))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:525
+msgid ""
+"Rename ProtoBuf messages to improve consistency "
+"([#1214](https://github.com/adap/flower/pull/1214), "
+"[#1258](https://github.com/adap/flower/pull/1258), "
+"[#1259](https://github.com/adap/flower/pull/1259))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:527
+msgid "v0.19.0 (2022-05-18)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:531
+msgid ""
+"**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** "
+"([#919](https://github.com/adap/flower/pull/919), "
+"[#1127](https://github.com/adap/flower/pull/1127), "
+"[#914](https://github.com/adap/flower/pull/914))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:533
+msgid ""
+"The first preview release of Flower Baselines has arrived! We're "
+"kickstarting Flower Baselines with implementations of FedOpt (FedYogi, "
+"FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how "
+"to use [Flower Baselines](https://flower.dev/docs/using-baselines.html). "
+"With this first preview release we're also inviting the community to "
+"[contribute their own baselines](https://flower.dev/docs/contributing-"
+"baselines.html)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:535
+msgid ""
+"**C++ client SDK (preview) and code example** "
+"([#1111](https://github.com/adap/flower/pull/1111))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:537
+msgid ""
+"Preview support for Flower clients written in C++. The C++ preview "
+"includes a Flower client SDK and a quickstart code example that "
+"demonstrates a simple C++ client using the SDK."
+msgstr ""
+
+#: ../../source/ref-changelog.md:539
+msgid ""
+"**Add experimental support for Python 3.10 and Python 3.11** "
+"([#1135](https://github.com/adap/flower/pull/1135))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:541
+msgid ""
+"Python 3.10 is the latest stable release of Python and Python 3.11 is due"
+" to be released in October. This Flower release adds experimental support"
+" for both Python versions."
+msgstr ""
+
+#: ../../source/ref-changelog.md:543
+msgid ""
+"**Aggregate custom metrics through user-provided functions** "
+"([#1144](https://github.com/adap/flower/pull/1144))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:545
+msgid ""
+"Custom metrics (e.g., `accuracy`) can now be aggregated without having to"
+" customize the strategy. Built-in strategies support two new arguments, "
+"`fit_metrics_aggregation_fn` and `evaluate_metrics_aggregation_fn`, that "
+"allow passing custom metric aggregation functions."
+msgstr ""
+
+#: ../../source/ref-changelog.md:547
+msgid ""
+"**User-configurable round timeout** "
+"([#1162](https://github.com/adap/flower/pull/1162))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:549
+msgid ""
+"A new configuration value allows the round timeout to be set for "
+"`start_server` and `start_simulation`. If the `config` dictionary "
+"contains a `round_timeout` key (with a `float` value in seconds), the "
+"server will wait *at least* `round_timeout` seconds before it closes the "
+"connection."
+msgstr ""
+
+#: ../../source/ref-changelog.md:551
+msgid ""
+"**Enable both federated evaluation and centralized evaluation to be used "
+"at the same time in all built-in strategies** "
+"([#1091](https://github.com/adap/flower/pull/1091))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:553
+msgid ""
+"Built-in strategies can now perform both federated evaluation (i.e., "
+"client-side) and centralized evaluation (i.e., server-side) in the same "
+"round. Federated evaluation can be disabled by setting `fraction_eval` to"
+" `0.0`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:555
+msgid ""
+"**Two new Jupyter Notebook tutorials** "
+"([#1141](https://github.com/adap/flower/pull/1141))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:557
+msgid ""
+"Two Jupyter Notebook tutorials (compatible with Google Colab) explain "
+"basic and intermediate Flower features:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:559
+msgid ""
+"*An Introduction to Federated Learning*: [Open in "
+"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1"
+"-Intro-to-FL-PyTorch.ipynb)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:561
+msgid ""
+"*Using Strategies in Federated Learning*: [Open in "
+"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2"
+"-Strategies-in-FL-PyTorch.ipynb)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:563
+msgid ""
+"**New FedAvgM strategy (Federated Averaging with Server Momentum)** "
+"([#1076](https://github.com/adap/flower/pull/1076))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:565
+msgid ""
+"The new `FedAvgM` strategy implements Federated Averaging with Server "
+"Momentum \\[Hsu et al., 2019\\]."
+msgstr ""
+
+#: ../../source/ref-changelog.md:567
+msgid ""
+"**New advanced PyTorch code example** "
+"([#1007](https://github.com/adap/flower/pull/1007))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:569
+msgid ""
+"A new code example (`advanced_pytorch`) demonstrates advanced Flower "
+"concepts with PyTorch."
+msgstr ""
+
+#: ../../source/ref-changelog.md:571
+msgid ""
+"**New JAX code example** "
+"([#906](https://github.com/adap/flower/pull/906), "
+"[#1143](https://github.com/adap/flower/pull/1143))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:573
+msgid ""
+"A new code example (`jax_from_centralized_to_federated`) shows federated "
+"learning with JAX and Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:577
+msgid ""
+"New option to keep Ray running if Ray was already initialized in "
+"`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:578
+msgid ""
+"Add support for custom `ClientManager` as a `start_simulation` parameter "
+"([#1171](https://github.com/adap/flower/pull/1171))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:579
+msgid ""
+"New documentation for [implementing "
+"strategies](https://flower.dev/docs/framework/how-to-implement-"
+"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), "
+"[#1175](https://github.com/adap/flower/pull/1175))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:580
+msgid ""
+"New mobile-friendly documentation theme "
+"([#1174](https://github.com/adap/flower/pull/1174))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:581
+msgid ""
+"Limit version range for (optional) `ray` dependency to include only "
+"compatible releases (`>=1.9.2,<1.12.0`) "
+"([#1205](https://github.com/adap/flower/pull/1205))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:585
+msgid ""
+"**Remove deprecated support for Python 3.6** "
+"([#871](https://github.com/adap/flower/pull/871))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:586
+msgid ""
+"**Remove deprecated KerasClient** "
+"([#857](https://github.com/adap/flower/pull/857))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:587
+msgid ""
+"**Remove deprecated no-op extra installs** "
+"([#973](https://github.com/adap/flower/pull/973))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:588
+msgid ""
+"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` "
+"([#869](https://github.com/adap/flower/pull/869))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:589
+msgid ""
+"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** "
+"([#1107](https://github.com/adap/flower/pull/1107))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:590
+msgid ""
+"**Remove deprecated DefaultStrategy strategy** "
+"([#1142](https://github.com/adap/flower/pull/1142))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:591
+msgid ""
+"**Remove deprecated support for eval_fn accuracy return value** "
+"([#1142](https://github.com/adap/flower/pull/1142))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:592
+msgid ""
+"**Remove deprecated support for passing initial parameters as NumPy "
+"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:594
+msgid "v0.18.0 (2022-02-28)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:598
+msgid ""
+"**Improved Virtual Client Engine compatibility with Jupyter Notebook / "
+"Google Colab** ([#866](https://github.com/adap/flower/pull/866), "
+"[#872](https://github.com/adap/flower/pull/872), "
+"[#833](https://github.com/adap/flower/pull/833), "
+"[#1036](https://github.com/adap/flower/pull/1036))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:600
+msgid ""
+"Simulations (using the Virtual Client Engine through `start_simulation`) "
+"now work more smoothly on Jupyter Notebooks (incl. Google Colab) after "
+"installing Flower with the `simulation` extra (`pip install "
+"flwr[simulation]`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:602
+msgid ""
+"**New Jupyter Notebook code example** "
+"([#833](https://github.com/adap/flower/pull/833))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:604
+msgid ""
+"A new code example (`quickstart_simulation`) demonstrates Flower "
+"simulations using the Virtual Client Engine through Jupyter Notebook "
+"(incl. Google Colab)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:606
+msgid ""
+"**Client properties (feature preview)** "
+"([#795](https://github.com/adap/flower/pull/795))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:608
+msgid ""
+"Clients can implement a new method `get_properties` to enable server-side"
+" strategies to query client properties."
+msgstr ""
+
+#: ../../source/ref-changelog.md:610
+msgid ""
+"**Experimental Android support with TFLite** "
+"([#865](https://github.com/adap/flower/pull/865))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:612
+msgid ""
+"Android support has finally arrived in `main`! Flower is both client-"
+"agnostic and framework-agnostic by design. One can integrate arbitrary "
+"client platforms and with this release, using Flower on Android has "
+"become a lot easier."
+msgstr ""
+
+#: ../../source/ref-changelog.md:614
+msgid ""
+"The example uses TFLite on the client side, along with a new "
+"`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are "
+"still experimental, but they are a first step towards a fully-fledged "
+"Android SDK and a unified `FedAvg` implementation that integrated the new"
+" functionality from `FedAvgAndroid`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:616
+msgid ""
+"**Make gRPC keepalive time user-configurable and decrease default "
+"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:618
+msgid ""
+"The default gRPC keepalive time has been reduced to increase the "
+"compatibility of Flower with more cloud environments (for example, "
+"Microsoft Azure). Users can configure the keepalive time to customize the"
+" gRPC stack based on specific requirements."
+msgstr ""
+
+#: ../../source/ref-changelog.md:620
+msgid ""
+"**New differential privacy example using Opacus and PyTorch** "
+"([#805](https://github.com/adap/flower/pull/805))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:622
+msgid ""
+"A new code example (`opacus`) demonstrates differentially-private "
+"federated learning with Opacus, PyTorch, and Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:624
+msgid ""
+"**New Hugging Face Transformers code example** "
+"([#863](https://github.com/adap/flower/pull/863))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:626
+msgid ""
+"A new code example (`quickstart_huggingface`) demonstrates usage of "
+"Hugging Face Transformers with Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:628
+msgid ""
+"**New MLCube code example** "
+"([#779](https://github.com/adap/flower/pull/779), "
+"[#1034](https://github.com/adap/flower/pull/1034), "
+"[#1065](https://github.com/adap/flower/pull/1065), "
+"[#1090](https://github.com/adap/flower/pull/1090))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:630
+msgid ""
+"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube "
+"with Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:632
+msgid ""
+"**SSL-enabled server and client** "
+"([#842](https://github.com/adap/flower/pull/842),  "
+"[#844](https://github.com/adap/flower/pull/844),  "
+"[#845](https://github.com/adap/flower/pull/845), "
+"[#847](https://github.com/adap/flower/pull/847), "
+"[#993](https://github.com/adap/flower/pull/993), "
+"[#994](https://github.com/adap/flower/pull/994))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:634
+msgid ""
+"SSL enables secure encrypted connections between clients and servers. "
+"This release open-sources the Flower secure gRPC implementation to make "
+"encrypted communication channels accessible to all Flower users."
+msgstr ""
+
+#: ../../source/ref-changelog.md:636
+msgid ""
+"**Updated** `FedAdam` **and** `FedYogi` **strategies** "
+"([#885](https://github.com/adap/flower/pull/885), "
+"[#895](https://github.com/adap/flower/pull/895))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:638
+msgid ""
+"`FedAdam` and `FedAdam` match the latest version of the Adaptive "
+"Federated Optimization paper."
+msgstr ""
+
+#: ../../source/ref-changelog.md:640
+msgid ""
+"**Initialize** `start_simulation` **with a list of client IDs** "
+"([#860](https://github.com/adap/flower/pull/860))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:642
+msgid ""
+"`start_simulation` can now be called with a list of client IDs "
+"(`clients_ids`, type: `List[str]`). Those IDs will be passed to the "
+"`client_fn` whenever a client needs to be initialized, which can make it "
+"easier to load data partitions that are not accessible through `int` "
+"identifiers."
+msgstr ""
+
+#: ../../source/ref-changelog.md:646
+msgid ""
+"Update `num_examples` calculation in PyTorch code examples in "
+"([#909](https://github.com/adap/flower/pull/909))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:647
+msgid ""
+"Expose Flower version through `flwr.__version__` "
+"([#952](https://github.com/adap/flower/pull/952))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:648
+msgid ""
+"`start_server` in `app.py` now returns a `History` object containing "
+"metrics from training ([#974](https://github.com/adap/flower/pull/974))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:649
+msgid ""
+"Make `max_workers` (used by `ThreadPoolExecutor`) configurable "
+"([#978](https://github.com/adap/flower/pull/978))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:650
+msgid ""
+"Increase sleep time after server start to three seconds in all code "
+"examples ([#1086](https://github.com/adap/flower/pull/1086))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:651
+msgid ""
+"Added a new FAQ section to the documentation "
+"([#948](https://github.com/adap/flower/pull/948))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:652
+msgid ""
+"And many more under-the-hood changes, library updates, documentation "
+"changes, and tooling improvements!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:656
+msgid ""
+"**Removed** `flwr_example` **and** `flwr_experimental` **from release "
+"build** ([#869](https://github.com/adap/flower/pull/869))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:658
+msgid ""
+"The packages `flwr_example` and `flwr_experimental` have been deprecated "
+"since Flower 0.12.0 and they are not longer included in Flower release "
+"builds. The associated extras (`baseline`, `examples-pytorch`, `examples-"
+"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in "
+"an upcoming release."
+msgstr ""
+
+#: ../../source/ref-changelog.md:660
+msgid "v0.17.0 (2021-09-24)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:664
+msgid ""
+"**Experimental virtual client engine** "
+"([#781](https://github.com/adap/flower/pull/781) "
+"[#790](https://github.com/adap/flower/pull/790) "
+"[#791](https://github.com/adap/flower/pull/791))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:666
+msgid ""
+"One of Flower's goals is to enable research at scale. This release "
+"enables a first (experimental) peek at a major new feature, codenamed the"
+" virtual client engine. Virtual clients enable simulations that scale to "
+"a (very) large number of clients on a single machine or compute cluster. "
+"The easiest way to test the new functionality is to look at the two new "
+"code examples called `quickstart_simulation` and `simulation_pytorch`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:668
+msgid ""
+"The feature is still experimental, so there's no stability guarantee for "
+"the API. It's also not quite ready for prime time and comes with a few "
+"known caveats. However, those who are curious are encouraged to try it "
+"out and share their thoughts."
+msgstr ""
+
+#: ../../source/ref-changelog.md:670
+msgid ""
+"**New built-in strategies** "
+"([#828](https://github.com/adap/flower/pull/828) "
+"[#822](https://github.com/adap/flower/pull/822))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:672
+msgid ""
+"FedYogi - Federated learning strategy using Yogi on server-side. "
+"Implementation based on https://arxiv.org/abs/2003.00295"
+msgstr ""
+
+#: ../../source/ref-changelog.md:673
+msgid ""
+"FedAdam - Federated learning strategy using Adam on server-side. "
+"Implementation based on https://arxiv.org/abs/2003.00295"
+msgstr ""
+
+#: ../../source/ref-changelog.md:675
+msgid ""
+"**New PyTorch Lightning code example** "
+"([#617](https://github.com/adap/flower/pull/617))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:677
+msgid ""
+"**New Variational Auto-Encoder code example** "
+"([#752](https://github.com/adap/flower/pull/752))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:679
+msgid ""
+"**New scikit-learn code example** "
+"([#748](https://github.com/adap/flower/pull/748))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:681
+msgid ""
+"**New experimental TensorBoard strategy** "
+"([#789](https://github.com/adap/flower/pull/789))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:685
+msgid ""
+"Improved advanced TensorFlow code example "
+"([#769](https://github.com/adap/flower/pull/769))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:686
+msgid ""
+"Warning when `min_available_clients` is misconfigured "
+"([#830](https://github.com/adap/flower/pull/830))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:687
+msgid ""
+"Improved gRPC server docs "
+"([#841](https://github.com/adap/flower/pull/841))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:688
+msgid ""
+"Improved error message in `NumPyClient` "
+"([#851](https://github.com/adap/flower/pull/851))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:689
+msgid ""
+"Improved PyTorch quickstart code example "
+"([#852](https://github.com/adap/flower/pull/852))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:693
+msgid ""
+"**Disabled final distributed evaluation** "
+"([#800](https://github.com/adap/flower/pull/800))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:695
+msgid ""
+"Prior behaviour was to perform a final round of distributed evaluation on"
+" all connected clients, which is often not required (e.g., when using "
+"server-side evaluation). The prior behaviour can be enabled by passing "
+"`force_final_distributed_eval=True` to `start_server`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:697
+msgid ""
+"**Renamed q-FedAvg strategy** "
+"([#802](https://github.com/adap/flower/pull/802))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:699
+msgid ""
+"The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect "
+"the notation given in the original paper (q-FFL is the optimization "
+"objective, q-FedAvg is the proposed solver). Note the the original (now "
+"deprecated) `QffedAvg` class is still available for compatibility reasons"
+" (it will be removed in a future release)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:701
+msgid ""
+"**Deprecated and renamed code example** `simulation_pytorch` **to** "
+"`simulation_pytorch_legacy` "
+"([#791](https://github.com/adap/flower/pull/791))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:703
+msgid ""
+"This example has been replaced by a new example. The new example is based"
+" on the experimental virtual client engine, which will become the new "
+"default way of doing most types of large-scale simulations in Flower. The"
+" existing example was kept for reference purposes, but it might be "
+"removed in the future."
+msgstr ""
+
+#: ../../source/ref-changelog.md:705
+msgid "v0.16.0 (2021-05-11)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:709
+msgid ""
+"**New built-in strategies** "
+"([#549](https://github.com/adap/flower/pull/549))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:711
+msgid "(abstract) FedOpt"
+msgstr ""
+
+#: ../../source/ref-changelog.md:712
+msgid "FedAdagrad"
+msgstr ""
+
+#: ../../source/ref-changelog.md:714
+msgid ""
+"**Custom metrics for server and strategies** "
+"([#717](https://github.com/adap/flower/pull/717))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:716
+msgid ""
+"The Flower server is now fully task-agnostic, all remaining instances of "
+"task-specific metrics (such as `accuracy`) have been replaced by custom "
+"metrics dictionaries. Flower 0.15 introduced the capability to pass a "
+"dictionary containing custom metrics from client to server. As of this "
+"release, custom metrics replace task-specific metrics on the server."
+msgstr ""
+
+#: ../../source/ref-changelog.md:718
+msgid ""
+"Custom metric dictionaries are now used in two user-facing APIs: they are"
+" returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and "
+"they enable evaluation functions passed to build-in strategies (via "
+"`eval_fn`) to return more than two evaluation metrics. Strategies can "
+"even return *aggregated* metrics dictionaries for the server to keep "
+"track of."
+msgstr ""
+
+#: ../../source/ref-changelog.md:720
+msgid ""
+"Stratey implementations should migrate their `aggregate_fit` and "
+"`aggregate_evaluate` methods to the new return type (e.g., by simply "
+"returning an empty `{}`), server-side evaluation functions should migrate"
+" from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:722
+msgid ""
+"Flower 0.15-style return types are deprecated (but still supported), "
+"compatibility will be removed in a future release."
+msgstr ""
+
+#: ../../source/ref-changelog.md:724
+msgid ""
+"**Migration warnings for deprecated functionality** "
+"([#690](https://github.com/adap/flower/pull/690))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:726
+msgid ""
+"Earlier versions of Flower were often migrated to new APIs, while "
+"maintaining compatibility with legacy APIs. This release introduces "
+"detailed warning messages if usage of deprecated APIs is detected. The "
+"new warning messages often provide details on how to migrate to more "
+"recent APIs, thus easing the transition from one release to another."
+msgstr ""
+
+#: ../../source/ref-changelog.md:728
+msgid ""
+"Improved docs and docstrings "
+"([#691](https://github.com/adap/flower/pull/691) "
+"[#692](https://github.com/adap/flower/pull/692) "
+"[#713](https://github.com/adap/flower/pull/713))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:730
+msgid "MXNet example and documentation"
+msgstr ""
+
+#: ../../source/ref-changelog.md:732
+msgid ""
+"FedBN implementation in example PyTorch: From Centralized To Federated "
+"([#696](https://github.com/adap/flower/pull/696) "
+"[#702](https://github.com/adap/flower/pull/702) "
+"[#705](https://github.com/adap/flower/pull/705))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:736
+msgid ""
+"**Serialization-agnostic server** "
+"([#721](https://github.com/adap/flower/pull/721))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:738
+msgid ""
+"The Flower server is now fully serialization-agnostic. Prior usage of "
+"class `Weights` (which represents parameters as deserialized NumPy "
+"ndarrays) was replaced by class `Parameters` (e.g., in `Strategy`). "
+"`Parameters` objects are fully serialization-agnostic and represents "
+"parameters as byte arrays, the `tensor_type` attributes indicates how "
+"these byte arrays should be interpreted (e.g., for "
+"serialization/deserialization)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:740
+msgid ""
+"Built-in strategies implement this approach by handling serialization and"
+" deserialization to/from `Weights` internally. Custom/3rd-party Strategy "
+"implementations should update to the slighly changed Strategy method "
+"definitions. Strategy authors can consult PR "
+"[#721](https://github.com/adap/flower/pull/721) to see how strategies can"
+" easily migrate to the new format."
+msgstr ""
+
+#: ../../source/ref-changelog.md:742
+msgid ""
+"Deprecated `flwr.server.Server.evaluate`, use "
+"`flwr.server.Server.evaluate_round` instead "
+"([#717](https://github.com/adap/flower/pull/717))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:744
+msgid "v0.15.0 (2021-03-12)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:748
+msgid ""
+"**Server-side parameter initialization** "
+"([#658](https://github.com/adap/flower/pull/658))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:750
+msgid ""
+"Model parameters can now be initialized on the server-side. Server-side "
+"parameter initialization works via a new `Strategy` method called "
+"`initialize_parameters`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:752
+msgid ""
+"Built-in strategies support a new constructor argument called "
+"`initial_parameters` to set the initial parameters. Built-in strategies "
+"will provide these initial parameters to the server on startup and then "
+"delete them to free the memory afterwards."
+msgstr ""
+
+#: ../../source/ref-changelog.md:771
+msgid ""
+"If no initial parameters are provided to the strategy, the server will "
+"continue to use the current behaviour (namely, it will ask one of the "
+"connected clients for its parameters and use these as the initial global "
+"parameters)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:773
+msgid "Deprecations"
+msgstr ""
+
+#: ../../source/ref-changelog.md:775
+msgid ""
+"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to "
+"`flwr.server.strategy.FedAvg`, which is equivalent)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:777
+msgid "v0.14.0 (2021-02-18)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:781
+msgid ""
+"**Generalized** `Client.fit` **and** `Client.evaluate` **return values** "
+"([#610](https://github.com/adap/flower/pull/610) "
+"[#572](https://github.com/adap/flower/pull/572) "
+"[#633](https://github.com/adap/flower/pull/633))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:783
+msgid ""
+"Clients can now return an additional dictionary mapping `str` keys to "
+"values of the following types: `bool`, `bytes`, `float`, `int`, `str`. "
+"This means one can return almost arbitrary values from `fit`/`evaluate` "
+"and make use of them on the server side!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:785
+msgid ""
+"This improvement also allowed for more consistent return types between "
+"`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, "
+"dict)` representing the loss, number of examples, and a dictionary "
+"holding arbitrary problem-specific values like accuracy."
+msgstr ""
+
+#: ../../source/ref-changelog.md:787
+msgid ""
+"In case you wondered: this feature is compatible with existing projects, "
+"the additional dictionary return value is optional. New code should "
+"however migrate to the new return types to be compatible with upcoming "
+"Flower releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, "
+"`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for "
+"details."
+msgstr ""
+
+#: ../../source/ref-changelog.md:789
+msgid ""
+"*Code example:* note the additional dictionary return values in both "
+"`FlwrClient.fit` and `FlwrClient.evaluate`:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:804
+msgid ""
+"**Generalized** `config` **argument in** `Client.fit` **and** "
+"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:806
+msgid ""
+"The `config` argument used to be of type `Dict[str, str]`, which means "
+"that dictionary values were expected to be strings. The new release "
+"generalizes this to enable values of the following types: `bool`, "
+"`bytes`, `float`, `int`, `str`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:808
+msgid ""
+"This means one can now pass almost arbitrary values to `fit`/`evaluate` "
+"using the `config` dictionary. Yay, no more `str(epochs)` on the server-"
+"side and `int(config[\"epochs\"])` on the client side!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:810
+msgid ""
+"*Code example:* note that the `config` dictionary now contains non-`str` "
+"values in both `Client.fit` and `Client.evaluate`:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:827
+msgid "v0.13.0 (2021-01-08)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:831
+msgid ""
+"New example: PyTorch From Centralized To Federated "
+"([#549](https://github.com/adap/flower/pull/549))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:832
+msgid "Improved documentation"
+msgstr ""
+
+#: ../../source/ref-changelog.md:833
+msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:834
+msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:835
+msgid ""
+"Updated examples documentation "
+"([#549](https://github.com/adap/flower/pull/549))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:836
+msgid ""
+"Removed obsolete documentation "
+"([#548](https://github.com/adap/flower/pull/548))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:838
+msgid "Bugfix:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:840
+msgid ""
+"`Server.fit` does not disconnect clients when finished, disconnecting the"
+" clients is now handled in `flwr.server.start_server` "
+"([#553](https://github.com/adap/flower/pull/553) "
+"[#540](https://github.com/adap/flower/issues/540))."
+msgstr ""
+
+#: ../../source/ref-changelog.md:842
+msgid "v0.12.0 (2020-12-07)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:844 ../../source/ref-changelog.md:860
+msgid "Important changes:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:846
+msgid ""
+"Added an example for embedded devices "
+"([#507](https://github.com/adap/flower/pull/507))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:847
+msgid ""
+"Added a new NumPyClient (in addition to the existing KerasClient) "
+"([#504](https://github.com/adap/flower/pull/504) "
+"[#508](https://github.com/adap/flower/pull/508))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:848
+msgid ""
+"Deprecated `flwr_example` package and started to migrate examples into "
+"the top-level `examples` directory "
+"([#494](https://github.com/adap/flower/pull/494) "
+"[#512](https://github.com/adap/flower/pull/512))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:850
+msgid "v0.11.0 (2020-11-30)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:852
+msgid "Incompatible changes:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:854
+msgid ""
+"Renamed strategy methods "
+"([#486](https://github.com/adap/flower/pull/486)) to unify the naming of "
+"Flower's public APIs. Other public methods/functions (e.g., every method "
+"in `Client`, but also `Strategy.evaluate`) do not use the `on_` prefix, "
+"which is why we're removing it from the four methods in Strategy. To "
+"migrate rename the following `Strategy` methods accordingly:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:855
+msgid "`on_configure_evaluate` => `configure_evaluate`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:856
+msgid "`on_aggregate_evaluate` => `aggregate_evaluate`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:857
+msgid "`on_configure_fit` => `configure_fit`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:858
+msgid "`on_aggregate_fit` => `aggregate_fit`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:862
+msgid ""
+"Deprecated `DefaultStrategy` "
+"([#479](https://github.com/adap/flower/pull/479)). To migrate use "
+"`FedAvg` instead."
+msgstr ""
+
+#: ../../source/ref-changelog.md:863
+msgid ""
+"Simplified examples and baselines "
+"([#484](https://github.com/adap/flower/pull/484))."
+msgstr ""
+
+#: ../../source/ref-changelog.md:864
+msgid ""
+"Removed presently unused `on_conclude_round` from strategy interface "
+"([#483](https://github.com/adap/flower/pull/483))."
+msgstr ""
+
+#: ../../source/ref-changelog.md:865
+msgid ""
+"Set minimal Python version to 3.6.1 instead of 3.6.9 "
+"([#471](https://github.com/adap/flower/pull/471))."
+msgstr ""
+
+#: ../../source/ref-changelog.md:866
+msgid ""
+"Improved `Strategy` docstrings "
+"([#470](https://github.com/adap/flower/pull/470))."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:2
+msgid "Example projects"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:4
+msgid ""
+"Flower comes with a number of usage examples. The examples demonstrate "
+"how Flower can be used to federate different kinds of existing machine "
+"learning pipelines, usually leveraging popular machine learning "
+"frameworks such as `PyTorch <https://pytorch.org/>`_ or `TensorFlow "
+"<https://www.tensorflow.org/>`_."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:11
+msgid ""
+"Flower usage examples used to be bundled with Flower in a package called "
+"``flwr_example``. We are migrating those examples to standalone projects "
+"to make them easier to use. All new examples are based in the directory "
+"`examples <https://github.com/adap/flower/tree/main/examples>`_."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:16
+msgid "The following examples are available as standalone projects."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:20
+msgid "Quickstart TensorFlow/Keras"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:22
+msgid ""
+"The TensorFlow/Keras quickstart example shows CIFAR-10 image "
+"classification with MobileNetV2:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:25
+msgid ""
+"`Quickstart TensorFlow (Code) "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-"
+"tensorflow>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:26
+msgid ""
+"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework"
+"/tutorial-quickstart-tensorflow.html>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:27
+msgid ""
+"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
+"-federated-learning-in-less-than-20-lines-of-code>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:31
+#: ../../source/tutorial-quickstart-pytorch.rst:5
+msgid "Quickstart PyTorch"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:33
+msgid ""
+"The PyTorch quickstart example shows CIFAR-10 image classification with a"
+" simple Convolutional Neural Network:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:36
+msgid ""
+"`Quickstart PyTorch (Code) "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:37
+msgid ""
+"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework"
+"/tutorial-quickstart-pytorch.html>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:41
+msgid "PyTorch: From Centralized To Federated"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:43
+msgid ""
+"This example shows how a regular PyTorch project can be federated using "
+"Flower:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:45
+msgid ""
+"`PyTorch: From Centralized To Federated (Code) "
+"<https://github.com/adap/flower/tree/main/examples/pytorch-from-"
+"centralized-to-federated>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:46
+msgid ""
+"`PyTorch: From Centralized To Federated (Tutorial) "
+"<https://flower.dev/docs/framework/example-pytorch-from-centralized-to-"
+"federated.html>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:50
+msgid "Federated Learning on Raspberry Pi and Nvidia Jetson"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:52
+msgid ""
+"This example shows how Flower can be used to build a federated learning "
+"system that run across Raspberry Pi and Nvidia Jetson:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:54
+msgid ""
+"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) "
+"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:55
+msgid ""
+"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) "
+"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:60
+msgid "Legacy Examples (`flwr_example`)"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:63
+msgid ""
+"The useage examples in `flwr_example` are deprecated and will be removed "
+"in the future. New examples are provided as standalone projects in "
+"`examples <https://github.com/adap/flower/tree/main/examples>`_."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:69
+msgid "Extra Dependencies"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:71
+msgid ""
+"The core Flower framework keeps a minimal set of dependencies. The "
+"examples demonstrate Flower in the context of different machine learning "
+"frameworks, so additional dependencies need to be installed before an "
+"example can be run."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:75
+msgid "For PyTorch examples::"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:79
+msgid "For TensorFlow examples::"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:83
+msgid "For both PyTorch and TensorFlow examples::"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:87
+msgid ""
+"Please consult :code:`pyproject.toml` for a full list of possible extras "
+"(section :code:`[tool.poetry.extras]`)."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:92
+msgid "PyTorch Examples"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:94
+msgid ""
+"Our PyTorch examples are based on PyTorch 1.7. They should work with "
+"other releases as well. So far, we provide the following examples."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:98
+msgid "CIFAR-10 Image Classification"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:100
+msgid ""
+"`CIFAR-10 and CIFAR-100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ "
+"are popular RGB image datasets. The Flower CIFAR-10 example uses PyTorch "
+"to train a simple CNN classifier in a federated learning setup with two "
+"clients."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:104
+#: ../../source/ref-example-projects.rst:121
+#: ../../source/ref-example-projects.rst:146
+msgid "First, start a Flower server:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:106
+msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:108
+#: ../../source/ref-example-projects.rst:125
+#: ../../source/ref-example-projects.rst:150
+msgid "Then, start the two clients in a new terminal window:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:110
+msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:112
+msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:115
+msgid "ImageNet-2012 Image Classification"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:117
+msgid ""
+"`ImageNet-2012 <http://www.image-net.org/>`_ is one of the major computer"
+" vision datasets. The Flower ImageNet example uses PyTorch to train a "
+"ResNet-18 classifier in a federated learning setup with ten clients."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:123
+msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:127
+msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:129
+msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:133
+msgid "TensorFlow Examples"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:135
+msgid ""
+"Our TensorFlow examples are based on TensorFlow 2.0 or newer. So far, we "
+"provide the following examples."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:139
+msgid "Fashion-MNIST Image Classification"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:141
+msgid ""
+"`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ is "
+"often used as the \"Hello, world!\" of machine learning. We follow this "
+"tradition and provide an example which samples random local datasets from"
+" Fashion-MNIST and trains a simple image classification model over those "
+"partitions."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:148
+msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:152
+msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:154
+msgid ""
+"For more details, see "
+":code:`src/py/flwr_example/tensorflow_fashion_mnist`."
+msgstr ""
+
+#: ../../source/ref-faq.rst:4
+msgid ""
+"This page collects answers to commonly asked questions about Federated "
+"Learning with Flower."
+msgstr ""
+
+#: ../../source/ref-faq.rst
+msgid ":fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?"
+msgstr ""
+
+#: ../../source/ref-faq.rst:8
+msgid ""
+"Yes, it can! Flower even comes with a few under-the-hood optimizations to"
+" make it work even better on Colab. Here's a quickstart example:"
+msgstr ""
+
+#: ../../source/ref-faq.rst:10
+msgid ""
+"`Flower simulation PyTorch "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
+"/simulation-pytorch/sim.ipynb>`_"
+msgstr ""
+
+#: ../../source/ref-faq.rst:11
+msgid ""
+"`Flower simulation TensorFlow/Keras "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
+"/simulation-tensorflow/sim.ipynb>`_"
+msgstr ""
+
+#: ../../source/ref-faq.rst
+msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?"
+msgstr ""
+
+#: ../../source/ref-faq.rst:15
+msgid ""
+"Find the `blog post about federated learning on embedded device here "
+"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+" and the corresponding `GitHub code example "
+"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
+msgstr ""
+
+#: ../../source/ref-faq.rst
+msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?"
+msgstr ""
+
+#: ../../source/ref-faq.rst:19
+msgid ""
+"Yes, it does. Please take a look at our `blog post "
+"<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
+"devices-with-flower>`_ or check out the code examples:"
+msgstr ""
+
+#: ../../source/ref-faq.rst:21
+msgid ""
+"`Android Kotlin example <https://flower.dev/docs/examples/android-"
+"kotlin.html>`_"
+msgstr ""
+
+#: ../../source/ref-faq.rst:22
+msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
+msgstr ""
+
+#: ../../source/ref-faq.rst
+msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?"
+msgstr ""
+
+#: ../../source/ref-faq.rst:26
+msgid ""
+"Yes, of course. A list of available examples using Flower within a "
+"blockchain environment is available here:"
+msgstr ""
+
+#: ../../source/ref-faq.rst:28
+msgid ""
+"`Flower meets Nevermined GitHub Repository <https://github.com"
+"/nevermined-io/fl-demo/tree/master/image-classification-flower>`_."
+msgstr ""
+
+#: ../../source/ref-faq.rst:29
+msgid ""
+"`Flower meets Nevermined YouTube video "
+"<https://www.youtube.com/watch?v=A0A9hSlPhKI>`_."
+msgstr ""
+
+#: ../../source/ref-faq.rst:30
+msgid ""
+"`Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-"
+"content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_."
+msgstr ""
+
+#: ../../source/ref-faq.rst:31
+msgid ""
+"`Flower meets Talan blog post <https://www.linkedin.com/pulse/federated-"
+"learning-same-mask-different-faces-imen-"
+"ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ ."
+msgstr ""
+
+#: ../../source/ref-faq.rst:32
+msgid ""
+"`Flower meets Talan GitHub Repository "
+"<https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ ."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:1
+msgid "Telemetry"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:3
+msgid ""
+"The Flower open-source project collects **anonymous** usage metrics to "
+"make well-informed decisions to improve Flower. Doing this enables the "
+"Flower team to understand how Flower is used and what challenges users "
+"might face."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:5
+msgid ""
+"**Flower is a friendly framework for collaborative AI and data science.**"
+" Staying true to this statement, Flower makes it easy to disable "
+"telemetry for users that do not want to share anonymous usage metrics."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:7
+msgid "Principles"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:9
+msgid "We follow strong principles guarding anonymous usage metrics collection:"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:11
+msgid ""
+"**Optional:** You will always be able to disable telemetry; read on to "
+"learn “[How to opt-out](#how-to-opt-out)”."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:12
+msgid ""
+"**Anonymous:** The reported usage metrics are anonymous and do not "
+"contain any personally identifiable information (PII). See “[Collected "
+"metrics](#collected-metrics)” to understand what metrics are being "
+"reported."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:13
+msgid ""
+"**Transparent:** You can easily inspect what anonymous metrics are being "
+"reported; see the section “[How to inspect what is being reported](#how-"
+"to-inspect-what-is-being-reported)”"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:14
+msgid ""
+"**Open for feedback:** You can always reach out to us if you have "
+"feedback; see the section “[How to contact us](#how-to-contact-us)” for "
+"details."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:16
+msgid "How to opt-out"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:18
+msgid ""
+"When Flower starts, it will check for an environment variable called "
+"`FLWR_TELEMETRY_ENABLED`. Telemetry can easily be disabled by setting "
+"`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or "
+"client, simply do so by prepending your command as in:"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:24
+msgid ""
+"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example,"
+" `.bashrc` (or whatever configuration file applies to your environment) "
+"to disable Flower telemetry permanently."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:26
+msgid "Collected metrics"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:28
+msgid "Flower telemetry collects the following metrics:"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:30
+msgid ""
+"**Flower version.** Understand which versions of Flower are currently "
+"being used. This helps us to decide whether we should invest effort into "
+"releasing a patch version for an older version of Flower or instead use "
+"the bandwidth to build new features."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:32
+msgid ""
+"**Operating system.** Enables us to answer questions such as: *Should we "
+"create more guides for Linux, macOS, or Windows?*"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:34
+msgid ""
+"**Python version.** Knowing the Python version helps us, for example, to "
+"decide whether we should invest effort into supporting old versions of "
+"Python or stop supporting them and start taking advantage of new Python "
+"features."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:36
+msgid ""
+"**Hardware properties.** Understanding the hardware environment that "
+"Flower is being used in helps to decide whether we should, for example, "
+"put more effort into supporting low-resource environments."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:38
+msgid ""
+"**Execution mode.** Knowing what execution mode Flower starts in enables "
+"us to understand how heavily certain features are being used and better "
+"prioritize based on that."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:40
+msgid ""
+"**Cluster.** Flower telemetry assigns a random in-memory cluster ID each "
+"time a Flower workload starts. This allows us to understand which device "
+"types not only start Flower workloads but also successfully complete "
+"them."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:42
+msgid ""
+"**Source.** Flower telemetry tries to store a random source ID in "
+"`~/.flwr/source` the first time a telemetry event is generated. The "
+"source ID is important to identify whether an issue is recurring or "
+"whether an issue is triggered by multiple clusters running concurrently "
+"(which often happens in simulation). For example, if a device runs "
+"multiple workloads at the same time, and this results in an issue, then, "
+"in order to reproduce the issue, multiple workloads must be started at "
+"the same time."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:44
+msgid ""
+"You may delete the source ID at any time. If you wish for all events "
+"logged under a specific source ID to be deleted, you can send a deletion "
+"request mentioning the source ID to `telemetry@flower.dev`. All events "
+"related to that source ID will then be permanently deleted."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:46
+msgid ""
+"We will not collect any personally identifiable information. If you think"
+" any of the metrics collected could be misused in any way, please [get in"
+" touch with us](#how-to-contact-us). We will update this page to reflect "
+"any changes to the metrics collected and publish changes in the "
+"changelog."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:48
+msgid ""
+"If you think other metrics would be helpful for us to better guide our "
+"decisions, please let us know! We will carefully review them; if we are "
+"confident that they do not compromise user privacy, we may add them."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:50
+msgid "How to inspect what is being reported"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:52
+msgid ""
+"We wanted to make it very easy for you to inspect what anonymous usage "
+"metrics are reported. You can view all the reported telemetry information"
+" by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging "
+"is disabled by default. You may use logging independently from "
+"`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature "
+"without sending any metrics."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:58
+msgid ""
+"The inspect Flower telemetry without sending any anonymous usage metrics,"
+" use both environment variables:"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:64
+msgid "How to contact us"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:66
+msgid ""
+"We want to hear from you. If you have any feedback or ideas on how to "
+"improve the way we handle anonymous usage metrics, reach out to us via "
+"[Slack](https://flower.dev/join-slack/) (channel `#telemetry`) or email "
+"(`telemetry@flower.dev`)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-android.rst:-1
+msgid ""
+"Read this Federated Learning quickstart tutorial for creating an Android "
+"app using Flower."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-android.rst:5
+msgid "Quickstart Android"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-android.rst:10
+msgid ""
+"Let's build a federated learning system using TFLite and Flower on "
+"Android!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-android.rst:12
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/android>`_ to learn "
+"more."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-fastai.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with FastAI to train a vision model on CIFAR-10."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-fastai.rst:5
+msgid "Quickstart fastai"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-fastai.rst:10
+msgid "Let's build a federated learning system using fastai and Flower!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-fastai.rst:12
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-fastai>`_ "
+"to learn more."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:-1
+msgid ""
+"Check out this Federating Learning quickstart tutorial for using Flower "
+"with HuggingFace Transformers in order to fine-tune an LLM."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:5
+msgid "Quickstart 🤗 Transformers"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:10
+msgid ""
+"Let's build a federated learning system using Hugging Face Transformers "
+"and Flower!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:12
+msgid ""
+"We will leverage Hugging Face to federate the training of language models"
+" over multiple clients using Flower. More specifically, we will fine-tune"
+" a pre-trained Transformer model (distilBERT) for sequence classification"
+" over a dataset of IMDB ratings. The end goal is to detect if a movie "
+"rating is positive or negative."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:18
+msgid "Dependencies"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:20
+msgid ""
+"To follow along this tutorial you will need to install the following "
+"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, "
+":code:`torch`, and :code:`transformers`. This can be done using "
+":code:`pip`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:30
+msgid "Standard Hugging Face workflow"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:33
+msgid "Handling the data"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:35
+msgid ""
+"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` "
+"library. We then need to tokenize the data and create :code:`PyTorch` "
+"dataloaders, this is all done in the :code:`load_data` function:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:81
+msgid "Training and testing the model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:83
+msgid ""
+"Once we have a way of creating our trainloader and testloader, we can "
+"take care of the training and testing. This is very similar to any "
+":code:`PyTorch` training or testing loop:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:121
+msgid "Creating the model itself"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:123
+msgid ""
+"To create the model itself, we will just load the pre-trained distillBERT"
+" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:136
+msgid "Federating the example"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:139
+msgid "Creating the IMDBClient"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:141
+msgid ""
+"To federate our example to multiple clients, we first need to write our "
+"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). "
+"This is very easy, as our model is a standard :code:`PyTorch` model:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:169
+msgid ""
+"The :code:`get_parameters` function lets the server get the client's "
+"parameters. Inversely, the :code:`set_parameters` function allows the "
+"server to send its parameters to the client. Finally, the :code:`fit` "
+"function trains the model locally for the client, and the "
+":code:`evaluate` function tests the model locally and returns the "
+"relevant metrics."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:175
+msgid "Starting the server"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:177
+msgid ""
+"Now that we have a way to instantiate clients, we need to create our "
+"server in order to aggregate the results. Using Flower, this can be done "
+"very easily by first choosing a strategy (here, we are using "
+":code:`FedAvg`, which will define the global weights as the average of "
+"all the clients' weights at each round) and then using the "
+":code:`flwr.server.start_server` function:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:205
+msgid ""
+"The :code:`weighted_average` function is there to provide a way to "
+"aggregate the metrics distributed amongst the clients (basically this "
+"allows us to display a nice average accuracy and loss for every round)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:209
+msgid "Putting everything together"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:211
+msgid "We can now start client instances using:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:221
+msgid ""
+"And they will be able to connect to the server and start the federated "
+"training."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:223
+msgid ""
+"If you want to check out everything put together, you should check out "
+"the full code example: [https://github.com/adap/flower/tree/main/examples"
+"/quickstart-"
+"huggingface](https://github.com/adap/flower/tree/main/examples"
+"/quickstart-huggingface)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:227
+msgid ""
+"Of course, this is a very basic example, and a lot can be added or "
+"modified, it was just to showcase how simply we could federate a Hugging "
+"Face workflow using Flower."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:230
+msgid ""
+"Note that in this example we used :code:`PyTorch`, but we could have very"
+" well used :code:`TensorFlow`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:-1
+msgid ""
+"Read this Federated Learning quickstart tutorial for creating an iOS app "
+"using Flower to train a neural network on MNIST."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:5
+msgid "Quickstart iOS"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:10
+msgid ""
+"In this tutorial we will learn how to train a Neural Network on MNIST "
+"using Flower and CoreML on iOS devices."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:12
+msgid ""
+"First of all, for running the Flower Python server, it is recommended to "
+"create a virtual environment and run everything within a `virtualenv "
+"<https://flower.dev/docs/recommended-env-setup.html>`_. For the Flower "
+"client implementation in iOS, it is recommended to use Xcode as our IDE."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:15
+msgid ""
+"Our example consists of one Python *server* and two iPhone *clients* that"
+" all have the same model."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:17
+msgid ""
+"*Clients* are responsible for generating individual weight updates for "
+"the model based on their local datasets. These updates are then sent to "
+"the *server* which will aggregate them to produce a better model. "
+"Finally, the *server* sends this improved version of the model back to "
+"each *client*. A complete cycle of weight updates is called a *round*."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:21
+msgid ""
+"Now that we have a rough idea of what is going on, let's get started to "
+"setup our Flower server environment. We first need to install Flower. You"
+" can do this by using pip:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:27
+msgid "Or Poetry:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:36
+msgid ""
+"Now that we have all our dependencies installed, let's run a simple "
+"distributed training using CoreML as our local training pipeline and "
+"MNIST as our dataset. For simplicity reasons we will use the complete "
+"Flower client with CoreML, that has been implemented and stored inside "
+"the Swift SDK. The client implementation can be seen below:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:72
+msgid ""
+"Let's create a new application project in Xcode and add :code:`flwr` as a"
+" dependency in your project. For our application, we will store the logic"
+" of our app in :code:`FLiOSModel.swift` and the UI elements in "
+":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`"
+" in this quickstart. Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/ios>`_ to learn more "
+"about the app."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:75
+msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:83
+msgid ""
+"Then add the mlmodel to the project simply by drag-and-drop, the mlmodel "
+"will be bundled inside the application during deployment to your iOS "
+"device. We need to pass the url to access mlmodel and run CoreML machine "
+"learning processes, it can be retrieved by calling the function "
+":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it "
+"into :code:`MLBatchProvider` object. The preprocessing is done inside "
+":code:`DataLoader.swift`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:99
+msgid ""
+"Since CoreML does not allow the model parameters to be seen before "
+"training, and accessing the model parameters during or after the training"
+" can only be done by specifying the layer name, we need to know this "
+"informations beforehand, through looking at the model specification, "
+"which are written as proto files. The implementation can be seen in "
+":code:`MLModelInspect`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:102
+msgid ""
+"After we have all of the necessary informations, let's create our Flower "
+"client."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:117
+msgid ""
+"Then start the Flower gRPC client and start communicating to the server "
+"by passing our Flower client to the function :code:`startFlwrGRPC`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:124
+msgid ""
+"That's it for the client. We only have to implement :code:`Client` or "
+"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. "
+"The attribute :code:`hostname` and :code:`port` tells the client which "
+"server to connect to. This can be done by entering the hostname and port "
+"in the application before clicking the start button to start the "
+"federated learning process."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:131
+#: ../../source/tutorial-quickstart-mxnet.rst:226
+#: ../../source/tutorial-quickstart-pytorch.rst:205
+#: ../../source/tutorial-quickstart-tensorflow.rst:100
+msgid ""
+"For simple workloads we can start a Flower server and leave all the "
+"configuration possibilities at their default values. In a file named "
+":code:`server.py`, import Flower and start the server:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:142
+#: ../../source/tutorial-quickstart-mxnet.rst:237
+#: ../../source/tutorial-quickstart-pytorch.rst:216
+#: ../../source/tutorial-quickstart-scikitlearn.rst:215
+#: ../../source/tutorial-quickstart-tensorflow.rst:112
+msgid "Train the model, federated!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:144
+#: ../../source/tutorial-quickstart-pytorch.rst:218
+#: ../../source/tutorial-quickstart-tensorflow.rst:114
+#: ../../source/tutorial-quickstart-xgboost.rst:522
+msgid ""
+"With both client and server ready, we can now run everything and see "
+"federated learning in action. FL systems usually have a server and "
+"multiple clients. We therefore have to start the server first:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:152
+msgid ""
+"Once the server is running we can start the clients in different "
+"terminals. Build and run the client through your Xcode, one through Xcode"
+" Simulator and the other by deploying it to your iPhone. To see more "
+"about how to deploy your app to iPhone or Simulator visit `here "
+"<https://developer.apple.com/documentation/xcode/running-your-app-in-"
+"simulator-or-on-a-device>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:156
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system in your ios device. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/ios>`_ for this "
+"example can be found in :code:`examples/ios`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-jax.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with Jax to train a linear regression model on a scikit-learn dataset."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-jax.rst:5
+msgid "Quickstart JAX"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with MXNet to train a Sequential model on MNIST."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:5
+msgid "Quickstart MXNet"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:10
+msgid ""
+"In this tutorial, we will learn how to train a :code:`Sequential` model "
+"on MNIST using Flower and MXNet."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:12
+#: ../../source/tutorial-quickstart-scikitlearn.rst:12
+msgid ""
+"It is recommended to create a virtual environment and run everything "
+"within this `virtualenv <https://flower.dev/docs/recommended-env-"
+"setup.html>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:16
+#: ../../source/tutorial-quickstart-scikitlearn.rst:16
+msgid ""
+"*Clients* are responsible for generating individual model parameter "
+"updates for the model based on their local datasets. These updates are "
+"then sent to the *server* which will aggregate them to produce an updated"
+" global model. Finally, the *server* sends this improved version of the "
+"model back to each *client*. A complete cycle of parameters updates is "
+"called a *round*."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:20
+#: ../../source/tutorial-quickstart-scikitlearn.rst:20
+msgid ""
+"Now that we have a rough idea of what is going on, let's get started. We "
+"first need to install Flower. You can do this by running:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:26
+msgid "Since we want to use MXNet, let's go ahead and install it:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:36
+msgid ""
+"Now that we have all our dependencies installed, let's run a simple "
+"distributed training with two clients and one server. Our training "
+"procedure and network architecture are based on MXNet´s `Hand-written "
+"Digit Recognition tutorial "
+"<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:38
+msgid ""
+"In a file called :code:`client.py`, import Flower and MXNet related "
+"packages:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:53
+msgid "In addition, define the device allocation in MXNet with:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:59
+msgid ""
+"We use MXNet to load MNIST, a popular image classification dataset of "
+"handwritten digits for machine learning. The MXNet utility "
+":code:`mx.test_utils.get_mnist()` downloads the training and test data."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:73
+msgid ""
+"Define the training and loss with MXNet. We train the model by looping "
+"over the dataset, measure the corresponding loss, and optimize it."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:111
+msgid ""
+"Next, we define the validation of our machine learning model. We loop "
+"over the test set and measure both loss and accuracy on the test set."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:135
+msgid ""
+"After defining the training and testing of a MXNet machine learning "
+"model, we use these functions to implement a Flower client."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:137
+msgid "Our Flower clients will use a simple :code:`Sequential` model:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:156
+msgid ""
+"After loading the dataset with :code:`load_data()` we perform one forward"
+" propagation to initialize the model and model parameters with "
+":code:`model(init)`. Next, we implement a Flower client."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:158
+#: ../../source/tutorial-quickstart-pytorch.rst:144
+#: ../../source/tutorial-quickstart-tensorflow.rst:54
+msgid ""
+"The Flower server interacts with clients through an interface called "
+":code:`Client`. When the server selects a particular client for training,"
+" it sends training instructions over the network. The client receives "
+"those instructions and calls one of the :code:`Client` methods to run "
+"your code (i.e., to train the neural network we defined earlier)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:164
+msgid ""
+"Flower provides a convenience class called :code:`NumPyClient` which "
+"makes it easier to implement the :code:`Client` interface when your "
+"workload uses MXNet. Implementing :code:`NumPyClient` usually means "
+"defining the following methods (:code:`set_parameters` is optional "
+"though):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:170
+#: ../../source/tutorial-quickstart-pytorch.rst:156
+#: ../../source/tutorial-quickstart-scikitlearn.rst:109
+msgid "return the model weight as a list of NumPy ndarrays"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:171
+#: ../../source/tutorial-quickstart-pytorch.rst:157
+#: ../../source/tutorial-quickstart-scikitlearn.rst:111
+msgid ":code:`set_parameters` (optional)"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:172
+#: ../../source/tutorial-quickstart-pytorch.rst:158
+#: ../../source/tutorial-quickstart-scikitlearn.rst:111
+msgid ""
+"update the local model weights with the parameters received from the "
+"server"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:174
+#: ../../source/tutorial-quickstart-pytorch.rst:160
+#: ../../source/tutorial-quickstart-scikitlearn.rst:114
+msgid "set the local model weights"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:175
+#: ../../source/tutorial-quickstart-pytorch.rst:161
+#: ../../source/tutorial-quickstart-scikitlearn.rst:115
+msgid "train the local model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:176
+#: ../../source/tutorial-quickstart-pytorch.rst:162
+#: ../../source/tutorial-quickstart-scikitlearn.rst:116
+msgid "receive the updated local model weights"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:178
+#: ../../source/tutorial-quickstart-pytorch.rst:164
+#: ../../source/tutorial-quickstart-scikitlearn.rst:118
+msgid "test the local model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:180
+msgid "They can be implemented in the following way:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:210
+msgid ""
+"We can now create an instance of our class :code:`MNISTClient` and add "
+"one line to actually run this client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:217
+#: ../../source/tutorial-quickstart-scikitlearn.rst:150
+msgid ""
+"That's it for the client. We only have to implement :code:`Client` or "
+":code:`NumPyClient` and call :code:`fl.client.start_client()` or "
+":code:`fl.client.start_numpy_client()`. The string "
+":code:`\"0.0.0.0:8080\"` tells the client which server to connect to. In "
+"our case we can run the server and the client on the same machine, "
+"therefore we use :code:`\"0.0.0.0:8080\"`. If we run a truly federated "
+"workload with the server and clients running on different machines, all "
+"that needs to change is the :code:`server_address` we pass to the client."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:239
+msgid ""
+"With both client and server ready, we can now run everything and see "
+"federated learning in action. Federated learning systems usually have a "
+"server and multiple clients. We therefore have to start the server first:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:247
+#: ../../source/tutorial-quickstart-pytorch.rst:226
+#: ../../source/tutorial-quickstart-scikitlearn.rst:224
+#: ../../source/tutorial-quickstart-tensorflow.rst:122
+#: ../../source/tutorial-quickstart-xgboost.rst:530
+msgid ""
+"Once the server is running we can start the clients in different "
+"terminals. Open a new terminal and start the first client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:254
+#: ../../source/tutorial-quickstart-pytorch.rst:233
+#: ../../source/tutorial-quickstart-scikitlearn.rst:231
+#: ../../source/tutorial-quickstart-tensorflow.rst:129
+#: ../../source/tutorial-quickstart-xgboost.rst:537
+msgid "Open another terminal and start the second client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:260
+#: ../../source/tutorial-quickstart-pytorch.rst:239
+#: ../../source/tutorial-quickstart-scikitlearn.rst:237
+#: ../../source/tutorial-quickstart-xgboost.rst:543
+msgid ""
+"Each client will have its own dataset. You should now see how the "
+"training does in the very first terminal (the one that started the "
+"server):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:292
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"mxnet/client.py>`_ for this example can be found in :code:`examples"
+"/quickstart-mxnet`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pandas.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with Pandas to perform Federated Analytics."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pandas.rst:5
+msgid "Quickstart Pandas"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pandas.rst:10
+msgid "Let's build a federated analytics system using Pandas and Flower!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pandas.rst:12
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
+"to learn more."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with PyTorch to train a CNN model on MNIST."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:13
+msgid ""
+"In this tutorial we will learn how to train a Convolutional Neural "
+"Network on CIFAR10 using Flower and PyTorch."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:15
+#: ../../source/tutorial-quickstart-xgboost.rst:36
+msgid ""
+"First of all, it is recommended to create a virtual environment and run "
+"everything within a `virtualenv <https://flower.dev/docs/recommended-env-"
+"setup.html>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:29
+msgid ""
+"Since we want to use PyTorch to solve a computer vision task, let's go "
+"ahead and install PyTorch and the **torchvision** library:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:39
+msgid ""
+"Now that we have all our dependencies installed, let's run a simple "
+"distributed training with two clients and one server. Our training "
+"procedure and network architecture are based on PyTorch's `Deep Learning "
+"with PyTorch "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:41
+msgid ""
+"In a file called :code:`client.py`, import Flower and PyTorch related "
+"packages:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:56
+msgid "In addition, we define the device allocation in PyTorch with:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:62
+msgid ""
+"We use PyTorch to load CIFAR10, a popular colored image classification "
+"dataset for machine learning. The PyTorch :code:`DataLoader()` downloads "
+"the training and test data that are then normalized."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:78
+msgid ""
+"Define the loss and optimizer with PyTorch. The training of the dataset "
+"is done by looping over the dataset, measure the corresponding loss and "
+"optimize it."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:94
+msgid ""
+"Define then the validation of the  machine learning network. We loop over"
+" the test set and measure the loss and accuracy of the test set."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:113
+msgid ""
+"After defining the training and testing of a PyTorch machine learning "
+"model, we use the functions for the Flower clients."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:115
+msgid ""
+"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 "
+"Minute Blitz':"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:142
+msgid ""
+"After loading the data set with :code:`load_data()` we define the Flower "
+"interface."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:150
+msgid ""
+"Flower provides a convenience class called :code:`NumPyClient` which "
+"makes it easier to implement the :code:`Client` interface when your "
+"workload uses PyTorch. Implementing :code:`NumPyClient` usually means "
+"defining the following methods (:code:`set_parameters` is optional "
+"though):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:166
+msgid "which can be implemented in the following way:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:189
+#: ../../source/tutorial-quickstart-tensorflow.rst:82
+msgid ""
+"We can now create an instance of our class :code:`CifarClient` and add "
+"one line to actually run this client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:196
+#: ../../source/tutorial-quickstart-tensorflow.rst:90
+msgid ""
+"That's it for the client. We only have to implement :code:`Client` or "
+":code:`NumPyClient` and call :code:`fl.client.start_client()` or "
+":code:`fl.client.start_numpy_client()`. The string :code:`\"[::]:8080\"` "
+"tells the client which server to connect to. In our case we can run the "
+"server and the client on the same machine, therefore we use "
+":code:`\"[::]:8080\"`. If we run a truly federated workload with the "
+"server and clients running on different machines, all that needs to "
+"change is the :code:`server_address` we point the client at."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:271
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"pytorch/client.py>`_ for this example can be found in :code:`examples"
+"/quickstart-pytorch`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with PyTorch Lightning to train an Auto Encoder model on MNIST."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5
+msgid "Quickstart PyTorch Lightning"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10
+msgid ""
+"Let's build a horizontal federated learning system using PyTorch "
+"Lightning and Flower!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-"
+"lightning>`_ to learn more."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with scikit-learn to train a linear regression model."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:5
+msgid "Quickstart scikit-learn"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:10
+msgid ""
+"In this tutorial, we will learn how to train a :code:`Logistic "
+"Regression` model on MNIST using Flower and scikit-learn."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:26
+msgid "Since we want to use scikt-learn, let's go ahead and install it:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:32
+msgid "Or simply install all dependencies using Poetry:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:42
+msgid ""
+"Now that we have all our dependencies installed, let's run a simple "
+"distributed training with two clients and one server. However, before "
+"setting up the client and server, we will define all functionalities that"
+" we need for our federated learning setup within :code:`utils.py`. The "
+":code:`utils.py` contains different functions defining all the machine "
+"learning basics:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:45
+msgid ":code:`get_model_parameters()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:46
+msgid "Returns the paramters of a :code:`sklearn` LogisticRegression model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:47
+msgid ":code:`set_model_params()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:48
+msgid "Sets the parameters of a :code:`sklean` LogisticRegression model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:49
+msgid ":code:`set_initial_params()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:50
+msgid "Initializes the model parameters that the Flower server will ask for"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:51
+msgid ":code:`load_mnist()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:52
+msgid "Loads the MNIST dataset using OpenML"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:53
+msgid ":code:`shuffle()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:54
+msgid "Shuffles data and its label"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:56
+msgid ":code:`partition()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:56
+msgid "Splits datasets into a number of partitions"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:58
+msgid ""
+"Please check out :code:`utils.py` `here "
+"<https://github.com/adap/flower/blob/main/examples/sklearn-logreg-"
+"mnist/utils.py>`_ for more details. The pre-defined functions are used in"
+" the :code:`client.py` and imported. The :code:`client.py` also requires "
+"to import several packages such as Flower and scikit-learn:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:73
+msgid ""
+"We load the MNIST dataset from `OpenML <https://www.openml.org/d/554>`_, "
+"a popular image classification dataset of handwritten digits for machine "
+"learning. The utility :code:`utils.load_mnist()` downloads the training "
+"and test data. The training set is split afterwards into 10 partitions "
+"with :code:`utils.partition()`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:85
+msgid ""
+"Next, the logistic regression model is defined and initialized with "
+":code:`utils.set_initial_params()`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:97
+msgid ""
+"The Flower server interacts with clients through an interface called "
+":code:`Client`. When the server selects a particular client for training,"
+" it sends training instructions over the network. The client receives "
+"those instructions and calls one of the :code:`Client` methods to run "
+"your code (i.e., to fit the logistic regression we defined earlier)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:103
+msgid ""
+"Flower provides a convenience class called :code:`NumPyClient` which "
+"makes it easier to implement the :code:`Client` interface when your "
+"workload uses scikit-learn. Implementing :code:`NumPyClient` usually "
+"means defining the following methods (:code:`set_parameters` is optional "
+"though):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:112
+msgid "is directly imported with :code:`utils.set_model_params()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:120
+msgid "The methods can be implemented in the following way:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:143
+msgid ""
+"We can now create an instance of our class :code:`MnistClient` and add "
+"one line to actually run this client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:159
+msgid ""
+"The following Flower server is a little bit more advanced and returns an "
+"evaluation function for the server-side evaluation. First, we import "
+"again all required libraries such as Flower and scikit-learn."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:162
+msgid ":code:`server.py`, import Flower and start the server:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:173
+msgid ""
+"The number of federated learning rounds is set in :code:`fit_round()` and"
+" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation "
+"function is called after each federated learning round and gives you "
+"information about loss and accuracy."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:198
+msgid ""
+"The :code:`main` contains the server-side parameter initialization "
+":code:`utils.set_initial_params()` as well as the aggregation strategy "
+":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, "
+"federated averaging (or FedAvg), with two clients and evaluation after "
+"each federated learning round. The server can be started with the command"
+" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", "
+"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:217
+msgid ""
+"With both client and server ready, we can now run everything and see "
+"federated learning in action. Federated learning systems usually have a "
+"server and multiple clients. We, therefore, have to start the server "
+"first:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:271
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/tree/main/examples/sklearn-logreg-"
+"mnist>`_ for this example can be found in :code:`examples/sklearn-logreg-"
+"mnist`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with TensorFlow to train a MobilNetV2 model on CIFAR-10."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:5
+msgid "Quickstart TensorFlow"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:13
+msgid "Let's build a federated learning system in less than 20 lines of code!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:15
+msgid "Before Flower can be imported we have to install it:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:21
+msgid ""
+"Since we want to use the Keras API of TensorFlow (TF), we have to install"
+" TF as well:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:31
+msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:38
+msgid ""
+"We use the Keras utilities of TF to load CIFAR10, a popular colored image"
+" classification dataset for machine learning. The call to "
+":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches "
+"it locally, and then returns the entire training and test set as NumPy "
+"ndarrays."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:47
+msgid ""
+"Next, we need a model. For the purpose of this tutorial, we use "
+"MobilNetV2 with 10 output classes:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:60
+msgid ""
+"Flower provides a convenience class called :code:`NumPyClient` which "
+"makes it easier to implement the :code:`Client` interface when your "
+"workload uses Keras. The :code:`NumPyClient` interface defines three "
+"methods which can be implemented in the following way:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:135
+msgid "Each client will have its own dataset."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:137
+msgid ""
+"You should now see how the training does in the very first terminal (the "
+"one that started the server):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:169
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"tensorflow/client.py>`_ for this can be found in :code:`examples"
+"/quickstart-tensorflow/client.py`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with XGBoost to train classification models on trees."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:5
+msgid "Quickstart XGBoost"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:11
+msgid "Federated XGBoost"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:13
+msgid ""
+"EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient "
+"implementation of gradient-boosted decision tree (**GBDT**), that "
+"maximises the computational boundaries for boosted tree methods. It's "
+"primarily designed to enhance both the performance and computational "
+"speed of machine learning models. In XGBoost, trees are constructed "
+"concurrently, unlike the sequential approach taken by GBDT."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:17
+msgid ""
+"Often, for tabular data on medium-sized datasets with fewer than 10k "
+"training examples, XGBoost surpasses the results of deep learning "
+"techniques."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:20
+msgid "Why federated XGBoost?"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:22
+msgid ""
+"Indeed, as the demand for data privacy and decentralized learning grows, "
+"there's an increasing requirement to implement federated XGBoost systems "
+"for specialised applications, like survival analysis and financial fraud "
+"detection."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:24
+msgid ""
+"Federated learning ensures that raw data remains on the local device, "
+"making it an attractive approach for sensitive domains where data "
+"security and privacy are paramount. Given the robustness and efficiency "
+"of XGBoost, combining it with federated learning offers a promising "
+"solution for these specific challenges."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:27
+msgid ""
+"In this tutorial we will learn how to train a federated XGBoost model on "
+"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple "
+"example (`full code xgboost-quickstart "
+"<https://github.com/adap/flower/tree/main/examples/xgboost-quickstart>`_)"
+" with two *clients* and one *server* to demonstrate how federated XGBoost"
+" works, and then we dive into a more complex example (`full code xgboost-"
+"comprehensive <https://github.com/adap/flower/tree/main/examples/xgboost-"
+"comprehensive>`_) to run various experiments."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:34
+msgid "Environment Setup"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:38
+msgid ""
+"We first need to install Flower and Flower Datasets. You can do this by "
+"running :"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:44
+msgid ""
+"Since we want to use :code:`xgboost` package to build up XGBoost trees, "
+"let's go ahead and install :code:`xgboost`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:54
+msgid ""
+"*Clients* are responsible for generating individual weight-updates for "
+"the model based on their local datasets. Now that we have all our "
+"dependencies installed, let's run a simple distributed training with two "
+"clients and one server."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:57
+msgid ""
+"In a file called :code:`client.py`, import xgboost, Flower, Flower "
+"Datasets and other related functions:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:84
+msgid "Dataset partition and hyper-parameter selection"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:86
+msgid ""
+"Prior to local training, we require loading the HIGGS dataset from Flower"
+" Datasets and conduct data partitioning for FL:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:99
+msgid ""
+"In this example, we split the dataset into two partitions with uniform "
+"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load "
+"the partition for the given client based on :code:`node_id`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:118
+msgid ""
+"After that, we do train/test splitting on the given partition (client's "
+"local data), and transform data format for :code:`xgboost` package."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:131
+msgid ""
+"The functions of :code:`train_test_split` and "
+":code:`transform_dataset_to_dmatrix` are defined as below:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:155
+msgid "Finally, we define the hyper-parameters used for XGBoost training."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:171
+msgid ""
+"The :code:`num_local_round` represents the number of iterations for local"
+" tree boost. We use CPU for the training in default. One can shift it to "
+"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as "
+"evaluation metric."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:178
+msgid "Flower client definition for XGBoost"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:180
+msgid ""
+"After loading the dataset we define the Flower client. We follow the "
+"general rule to define :code:`XgbClient` class inherited from "
+":code:`fl.client.Client`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:190
+msgid ""
+"The :code:`self.bst` is used to keep the Booster objects that remain "
+"consistent across rounds, allowing them to store predictions from trees "
+"integrated in earlier rounds and maintain other essential data structures"
+" for training."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:193
+msgid ""
+"Then, we override :code:`get_parameters`, :code:`fit` and "
+":code:`evaluate` methods insides :code:`XgbClient` class as follows."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:207
+msgid ""
+"Unlike neural network training, XGBoost trees are not started from a "
+"specified random weights. In this case, we do not use "
+":code:`get_parameters` and :code:`set_parameters` to initialise model "
+"parameters for XGBoost. As a result, let's return an empty tensor in "
+":code:`get_parameters` when it is called by the server at the first "
+"round."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:248
+msgid ""
+"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build "
+"up the first set of trees. the returned Booster object and config are "
+"stored in :code:`self.bst` and :code:`self.config`, respectively. From "
+"the second round, we load the global model sent from server to "
+":code:`self.bst`, and then update model weights on local training data "
+"with function :code:`local_boost` as follows:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:266
+msgid ""
+"Given :code:`num_local_round`, we update trees by calling "
+":code:`self.bst.update` method. After training, the last "
+":code:`N=num_local_round` trees will be extracted to send to the server."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:288
+msgid ""
+"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to "
+"conduct evaluation on valid set. The AUC value will be returned."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:291
+msgid ""
+"Now, we can create an instance of our class :code:`XgbClient` and add one"
+" line to actually run this client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:297
+msgid ""
+"That's it for the client. We only have to implement :code:`Client`and "
+"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` "
+"tells the client which server to connect to. In our case we can run the "
+"server and the client on the same machine, therefore we use "
+":code:`\"[::]:8080\"`. If we run a truly federated workload with the "
+"server and clients running on different machines, all that needs to "
+"change is the :code:`server_address` we point the client at."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:308
+msgid ""
+"These updates are then sent to the *server* which will aggregate them to "
+"produce a better model. Finally, the *server* sends this improved version"
+" of the model back to each *client* to finish a complete FL round."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:311
+msgid ""
+"In a file named :code:`server.py`, import Flower and FedXgbBagging from "
+":code:`flwr.server.strategy`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:313
+msgid "We first define a strategy for XGBoost bagging aggregation."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:336
+msgid ""
+"We use two clients for this example. An "
+":code:`evaluate_metrics_aggregation` function is defined to collect and "
+"wighted average the AUC values from clients."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:339
+msgid "Then, we start the server:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:351
+msgid "Tree-based bagging aggregation"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:353
+msgid ""
+"You must be curious about how bagging aggregation works. Let's look into "
+"the details."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:355
+msgid ""
+"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define "
+":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`."
+" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` "
+"and :code:`evaluate` methods as follows:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:451
+msgid ""
+"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost "
+"trees by calling :code:`aggregate()` function:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:510
+msgid ""
+"In this function, we first fetch the number of trees and the number of "
+"parallel trees for the current and previous model by calling "
+":code:`_get_tree_nums`. Then, the fetched information will be aggregated."
+" After that, the trees (containing model weights) are aggregated to "
+"generate a new tree model."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:515
+msgid ""
+"After traversal of all clients' models, a new global model is generated, "
+"followed by the serialisation, and sending back to each client."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:520
+msgid "Launch Federated XGBoost!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:582
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"XGBoost system. The AUC values can be checked in "
+":code:`metrics_distributed`. One can see that the average AUC increases "
+"over FL rounds."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:587
+msgid ""
+"The full `source code <https://github.com/adap/flower/blob/main/examples"
+"/xgboost-quickstart/>`_ for this example can be found in :code:`examples"
+"/xgboost-quickstart`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:591
+msgid "Comprehensive Federated XGBoost"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:593
+msgid ""
+"Now that you have known how federated XGBoost work with Flower, it's time"
+" to run some more comprehensive experiments by customising the "
+"experimental settings. In the xgboost-comprehensive example (`full code "
+"<https://github.com/adap/flower/tree/main/examples/xgboost-"
+"comprehensive>`_), we provide more options to define various experimental"
+" setups, including data partitioning and centralised/distributed "
+"evaluation. Let's take a look!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:599
+msgid "Customised data partitioning"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:601
+msgid ""
+"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`"
+" to instantiate the data partitioner based on the given "
+":code:`num_partitions` and :code:`partitioner_type`. Currently, we "
+"provide four supported partitioner type to simulate the uniformity/non-"
+"uniformity in data quantity (uniform, linear, square, exponential)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:632
+msgid "Customised centralised/distributed evaluation"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:634
+msgid ""
+"To facilitate centralised evaluation, we define a function in "
+":code:`server.py`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:666
+msgid ""
+"This function returns a evaluation function which instantiates a "
+":code:`Booster` object and loads the global model weights to it. The "
+"evaluation is conducted by calling :code:`eval_set()` method, and the "
+"tested AUC value is reported."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:669
+msgid ""
+"As for distributed evaluation on the clients, it's same as the quick-"
+"start example by overriding the :code:`evaluate()` method insides the "
+":code:`XgbClient` class in :code:`client.py`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:673
+msgid "Arguments parser"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:675
+msgid ""
+"In :code:`utils.py`, we define the arguments parsers for clients and "
+"server, allowing users to specify different experimental settings. Let's "
+"first see the sever side:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:714
+msgid ""
+"This allows user to specify the number of total clients / FL rounds / "
+"participating clients / clients for evaluation, and evaluation fashion. "
+"Note that with :code:`--centralised-eval`, the sever will do centralised "
+"evaluation and all functionalities for client evaluation will be "
+"disabled."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:718
+msgid "Then, the argument parser on client side:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:760
+msgid ""
+"This defines various options for client data partitioning. Besides, "
+"clients also have a option to conduct evaluation on centralised test set "
+"by setting :code:`--centralised-eval`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:764
+msgid "Example commands"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:766
+msgid ""
+"To run a centralised evaluated experiment on 5 clients with exponential "
+"distribution for 50 rounds, we first start the server as below:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:773
+msgid "Then, on each client terminal, we start the clients:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:779
+msgid ""
+"The full `source code <https://github.com/adap/flower/blob/main/examples"
+"/xgboost-comprehensive/>`_ for this comprehensive example can be found in"
+" :code:`examples/xgboost-comprehensive`."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9
+msgid "Build a strategy from scratch"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:11
+msgid ""
+"Welcome to the third part of the Flower federated learning tutorial. In "
+"previous parts of this tutorial, we introduced federated learning with "
+"PyTorch and Flower (`part 1 <https://flower.dev/docs/framework/tutorial-"
+"get-started-with-flower-pytorch.html>`__) and we learned how strategies "
+"can be used to customize the execution on both the server and the clients"
+" (`part 2 <https://flower.dev/docs/framework/tutorial-use-a-federated-"
+"learning-strategy-pytorch.html>`__)."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13
+msgid ""
+"In this notebook, we'll continue to customize the federated learning "
+"system we built previously by creating a custom version of FedAvg (again,"
+" using `Flower <https://flower.dev/>`__ and `PyTorch "
+"<https://pytorch.org/>`__)."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:15
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:15
+msgid ""
+"`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
+"the Flower community on Slack to connect, ask questions, and get help: "
+"`Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to hear from "
+"you in the ``#introductions`` channel! And if anything is unclear, head "
+"over to the ``#questions`` channel."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17
+msgid "Let's build a new ``Strategy`` from scratch!"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:29
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:29
+msgid "Preparation"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:31
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:32
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:31
+msgid ""
+"Before we begin with the actual code, let's make sure that we have "
+"everything we need."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:43
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:44
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:43
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:43
+msgid "Installing dependencies"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:45
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:46
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:45
+msgid "First, we install the necessary packages:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:65
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:66
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:65
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:65
+msgid ""
+"Now that we have all dependencies installed, we can import everything we "
+"need for this tutorial:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:104
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:101
+msgid ""
+"It is possible to switch to a runtime that has GPU acceleration enabled "
+"(on Google Colab: ``Runtime > Change runtime type > Hardware acclerator: "
+"GPU > Save``). Note, however, that Google Colab is not always able to "
+"offer GPU acceleration. If you see an error related to GPU availability "
+"in one of the following sections, consider switching back to CPU-based "
+"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime "
+"has GPU acceleration enabled, you should see the output ``Training on "
+"cuda``, otherwise it'll say ``Training on cpu``."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:114
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:115
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:114
+msgid "Data loading"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116
+msgid ""
+"Let's now load the CIFAR-10 training and test set, partition them into "
+"ten smaller datasets (each split into training and validation set), and "
+"wrap everything in their own ``DataLoader``. We introduce a new parameter"
+" ``num_clients`` which allows us to call ``load_datasets`` with different"
+" numbers of clients."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:167
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:168
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:167
+msgid "Model training/evaluation"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:169
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:169
+msgid ""
+"Let's continue with the usual model definition (including "
+"``set_parameters`` and ``get_parameters``), training and test functions:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:258
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:258
+msgid "Flower client"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260
+msgid ""
+"To implement the Flower client, we (again) create a subclass of "
+"``flwr.client.NumPyClient`` and implement the three methods "
+"``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the "
+"``cid`` to the client and use it log additional details:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308
+msgid "Let's test what we have so far before we continue:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:339
+msgid "Build a Strategy from scratch"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341
+msgid ""
+"Let’s overwrite the ``configure_fit`` method such that it passes a higher"
+" learning rate (potentially also other hyperparameters) to the optimizer "
+"of a fraction of the clients. We will keep the sampling of the clients as"
+" it is in ``FedAvg`` and then change the configuration dictionary (one of"
+" the ``FitIns`` attributes)."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:507
+msgid ""
+"The only thing left is to use the newly created custom Strategy "
+"``FedCustom`` when starting the experiment:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:534
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:932
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:697
+msgid "Recap"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536
+msgid ""
+"In this notebook, we’ve seen how to implement a custom strategy. A custom"
+" strategy enables granular control over client node configuration, result"
+" aggregation, and more. To define a custom strategy, you only have to "
+"overwrite the abstract methods of the (abstract) base class ``Strategy``."
+" To make custom strategies even more powerful, you can pass custom "
+"functions to the constructor of your new class (``__init__``) and then "
+"call these functions whenever needed."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:948
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:749
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:715
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:369
+msgid ""
+"Before you continue, make sure to join the Flower community on Slack: "
+"`Join Slack <https://flower.dev/join-slack/>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:751
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:717
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:371
+msgid ""
+"There's a dedicated ``#questions`` channel if you need help, but we'd "
+"also love to hear who you are in ``#introductions``!"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554
+msgid ""
+"The `Flower Federated Learning Tutorial - Part 4 "
+"<https://flower.dev/docs/framework/tutorial-customize-the-client-"
+"pytorch.html>`__ introduces ``Client``, the flexible API underlying "
+"``NumPyClient``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9
+msgid "Customize the client"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11
+msgid ""
+"Welcome to the fourth part of the Flower federated learning tutorial. In "
+"the previous parts of this tutorial, we introduced federated learning "
+"with PyTorch and Flower (`part 1 <https://flower.dev/docs/framework"
+"/tutorial-get-started-with-flower-pytorch.html>`__), we learned how "
+"strategies can be used to customize the execution on both the server and "
+"the clients (`part 2 <https://flower.dev/docs/framework/tutorial-use-a"
+"-federated-learning-strategy-pytorch.html>`__), and we built our own "
+"custom strategy from scratch (`part 3 <https://flower.dev/docs/framework"
+"/tutorial-build-a-strategy-from-scratch-pytorch.html>`__)."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14
+msgid ""
+"In this notebook, we revisit ``NumPyClient`` and introduce a new "
+"baseclass for building clients, simply named ``Client``. In previous "
+"parts of this tutorial, we've based our client on ``NumPyClient``, a "
+"convenience class which makes it easy to work with machine learning "
+"libraries that have good NumPy interoperability. With ``Client``, we gain"
+" a lot of flexibility that we didn't have before, but we'll also have to "
+"do a few things the we didn't have to do before."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:18
+msgid ""
+"Let's go deeper and see what it takes to move from ``NumPyClient`` to "
+"``Client``!"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:30
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:29
+msgid "Step 0: Preparation"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117
+msgid ""
+"Let's now load the CIFAR-10 training and test set, partition them into "
+"ten smaller datasets (each split into training and validation set), and "
+"wrap everything in their own ``DataLoader``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:259
+msgid "Step 1: Revisiting NumPyClient"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261
+msgid ""
+"So far, we've implemented our client by subclassing "
+"``flwr.client.NumPyClient``. The three methods we implemented are "
+"``get_parameters``, ``fit``, and ``evaluate``. Finally, we wrap the "
+"creation of instances of this class in a function called ``client_fn``:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:309
+msgid ""
+"We've seen this before, there's nothing new so far. The only *tiny* "
+"difference compared to the previous notebook is naming, we've changed "
+"``FlowerClient`` to ``FlowerNumPyClient`` and ``client_fn`` to "
+"``numpyclient_fn``. Let's run it to see the output we get:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:339
+msgid ""
+"This works as expected, two clients are training for three rounds of "
+"federated learning."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:341
+msgid ""
+"Let's dive a little bit deeper and discuss how Flower executes this "
+"simulation. Whenever a client is selected to do some work, "
+"``start_simulation`` calls the function ``numpyclient_fn`` to create an "
+"instance of our ``FlowerNumPyClient`` (along with loading the model and "
+"the data)."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:343
+msgid ""
+"But here's the perhaps surprising part: Flower doesn't actually use the "
+"``FlowerNumPyClient`` object directly. Instead, it wraps the object to "
+"makes it look like a subclass of ``flwr.client.Client``, not "
+"``flwr.client.NumPyClient``. In fact, the Flower core framework doesn't "
+"know how to handle ``NumPyClient``'s, it only knows how to handle "
+"``Client``'s. ``NumPyClient`` is just a convenience abstraction built on "
+"top of ``Client``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:345
+msgid ""
+"Instead of building on top of ``NumPyClient``, we can directly build on "
+"top of ``Client``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:357
+msgid "Step 2: Moving from ``NumPyClient`` to ``Client``"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:359
+msgid ""
+"Let's try to do the same thing using ``Client`` instead of "
+"``NumPyClient``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:465
+msgid ""
+"Before we discuss the code in more detail, let's try to run it! Gotta "
+"make sure our new ``Client``-based client works, right?"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:490
+msgid ""
+"That's it, we're now using ``Client``. It probably looks similar to what "
+"we've done with ``NumPyClient``. So what's the difference?"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:492
+msgid ""
+"First of all, it's more code. But why? The difference comes from the fact"
+" that ``Client`` expects us to take care of parameter serialization and "
+"deserialization. For Flower to be able to send parameters over the "
+"network, it eventually needs to turn these parameters into ``bytes``. "
+"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called "
+"serialization. Turning raw bytes into something more useful (like NumPy "
+"``ndarray``'s) is called deserialization. Flower needs to do both: it "
+"needs to serialize parameters on the server-side and send them to the "
+"client, the client needs to deserialize them to use them for local "
+"training, and then serialize the updated parameters again to send them "
+"back to the server, which (finally!) deserializes them again in order to "
+"aggregate them with the updates received from other clients."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:495
+msgid ""
+"The only *real* difference between Client and NumPyClient is that "
+"NumPyClient takes care of serialization and deserialization for you. It "
+"can do so because it expects you to return parameters as NumPy ndarray's,"
+" and it knows how to handle these. This makes working with machine "
+"learning libraries that have good NumPy support (most of them) a breeze."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:497
+msgid ""
+"In terms of API, there's one major difference: all methods in Client take"
+" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return "
+"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in "
+"``NumPyClient`` on the other hand have multiple arguments (e.g., "
+"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return"
+" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in "
+"``NumPyClient.fit``) if there are multiple things to handle. These "
+"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual "
+"values you're used to from ``NumPyClient``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:510
+msgid "Step 3: Custom serialization"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:512
+msgid ""
+"Here we will explore how to implement custom serialization with a simple "
+"example."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:514
+msgid ""
+"But first what is serialization? Serialization is just the process of "
+"converting an object into raw bytes, and equally as important, "
+"deserialization is the process of converting raw bytes back into an "
+"object. This is very useful for network communication. Indeed, without "
+"serialization, you could not just a Python object through the internet."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:516
+msgid ""
+"Federated Learning relies heavily on internet communication for training "
+"by sending Python objects back and forth between the clients and the "
+"server. This means that serialization is an essential part of Federated "
+"Learning."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:518
+msgid ""
+"In the following section, we will write a basic example where instead of "
+"sending a serialized version of our ``ndarray``\\ s containing our "
+"parameters, we will first convert the ``ndarray`` into sparse matrices, "
+"before sending them. This technique can be used to save bandwidth, as in "
+"certain cases where the weights of a model are sparse (containing many 0 "
+"entries), converting them to a sparse matrix can greatly improve their "
+"bytesize."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:521
+msgid "Our custom serialization/deserialization functions"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:523
+msgid ""
+"This is where the real serialization/deserialization will happen, "
+"especially in ``ndarray_to_sparse_bytes`` for serialization and "
+"``sparse_bytes_to_ndarray`` for deserialization."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:525
+msgid ""
+"Note that we imported the ``scipy.sparse`` library in order to convert "
+"our arrays."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:613
+msgid "Client-side"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615
+msgid ""
+"To be able to able to serialize our ``ndarray``\\ s into sparse "
+"parameters, we will just have to call our custom functions in our "
+"``flwr.client.Client``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:617
+msgid ""
+"Indeed, in ``get_parameters`` we need to serialize the parameters we got "
+"from our network using our custom ``ndarrays_to_sparse_parameters`` "
+"defined above."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:619
+msgid ""
+"In ``fit``, we first need to deserialize the parameters coming from the "
+"server using our custom ``sparse_parameters_to_ndarrays`` and then we "
+"need to serialize our local results with "
+"``ndarrays_to_sparse_parameters``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:621
+msgid ""
+"In ``evaluate``, we will only need to deserialize the global parameters "
+"with our custom function."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:725
+msgid "Server-side"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727
+msgid ""
+"For this example, we will just use ``FedAvg`` as a strategy. To change "
+"the serialization and deserialization here, we only need to reimplement "
+"the ``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other"
+" functions of the strategy will be inherited from the super class "
+"``FedAvg``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:729
+msgid "As you can see only one line as change in ``evaluate``:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:735
+msgid ""
+"And for ``aggregate_fit``, we will first deserialize every result we "
+"received:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:744
+msgid "And then serialize the aggregated result:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:903
+msgid "We can now run our custom serialization example!"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:934
+msgid ""
+"In this part of the tutorial, we've seen how we can build clients by "
+"subclassing either ``NumPyClient`` or ``Client``. ``NumPyClient`` is a "
+"convenience abstraction that makes it easier to work with machine "
+"learning libraries that have good NumPy interoperability. ``Client`` is a"
+" more flexible abstraction that allows us to do things that are not "
+"possible in ``NumPyClient``. In order to do so, it requires us to handle "
+"parameter serialization and deserialization ourselves."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:952
+msgid ""
+"This is the final part of the Flower tutorial (for now!), "
+"congratulations! You're now well equipped to understand the rest of the "
+"documentation. There are many topics we didn't cover in the tutorial, we "
+"recommend the following resources:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954
+msgid "`Read Flower Docs <https://flower.dev/docs/>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955
+msgid ""
+"`Check out Flower Code Examples "
+"<https://github.com/adap/flower/tree/main/examples>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956
+msgid ""
+"`Use Flower Baselines for your research "
+"<https://flower.dev/docs/baselines/>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957
+msgid ""
+"`Watch Flower Summit 2023 videos <https://flower.dev/conf/flower-"
+"summit-2023/>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9
+msgid "Get started with Flower"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:11
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:11
+msgid "Welcome to the Flower federated learning tutorial!"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13
+msgid ""
+"In this notebook, we'll build a federated learning system using Flower "
+"and PyTorch. In part 1, we use PyTorch for the model training pipeline "
+"and data loading. In part 2, we continue to federate the PyTorch-based "
+"pipeline using Flower."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:17
+msgid "Let's get stated!"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:31
+msgid ""
+"Before we begin with any actual code, let's make sure that we have "
+"everything we need."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45
+msgid ""
+"Next, we install the necessary packages for PyTorch (``torch`` and "
+"``torchvision``) and Flower (``flwr``):"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:117
+msgid "Loading the data"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:119
+msgid ""
+"Federated learning can be applied to many different types of tasks across"
+" different domains. In this tutorial, we introduce federated learning by "
+"training a simple convolutional neural network (CNN) on the popular "
+"CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that "
+"distinguish between images from ten different classes:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:150
+msgid ""
+"We simulate having multiple datasets from multiple organizations (also "
+"called the \"cross-silo\" setting in federated learning) by splitting the"
+" original CIFAR-10 dataset into multiple partitions. Each partition will "
+"represent the data from a single organization. We're doing this purely "
+"for experimentation purposes, in the real world there's no need for data "
+"splitting because each organization already has their own data (so the "
+"data is naturally partitioned)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:152
+msgid ""
+"Each organization will act as a client in the federated learning system. "
+"So having ten organizations participate in a federation means having ten "
+"clients connected to the federated learning server:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:172
+msgid ""
+"Let's now load the CIFAR-10 training and test set, partition them into "
+"ten smaller datasets (each split into training and validation set), and "
+"wrap the resulting partitions by creating a PyTorch ``DataLoader`` for "
+"each of them:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:222
+msgid ""
+"We now have a list of ten training sets and ten validation sets "
+"(``trainloaders`` and ``valloaders``) representing the data of ten "
+"different organizations. Each ``trainloader``/``valloader`` pair contains"
+" 4500 training examples and 500 validation examples. There's also a "
+"single ``testloader`` (we did not split the test set). Again, this is "
+"only necessary for building research or educational systems, actual "
+"federated learning systems have their data naturally distributed across "
+"multiple partitions."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:225
+msgid ""
+"Let's take a look at the first batch of images and labels in the first "
+"training set (i.e., ``trainloaders[0]``) before we move on:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:264
+msgid ""
+"The output above shows a random batch of images from the first "
+"``trainloader`` in our list of ten ``trainloaders``. It also prints the "
+"labels associated with each image (i.e., one of the ten possible labels "
+"we've seen above). If you run the cell again, you should see another "
+"batch of images."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:276
+msgid "Step 1: Centralized Training with PyTorch"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:287
+msgid ""
+"Next, we're going to use PyTorch to define a simple convolutional neural "
+"network. This introduction assumes basic familiarity with PyTorch, so it "
+"doesn't cover the PyTorch-related aspects in full detail. If you want to "
+"dive deeper into PyTorch, we recommend `DEEP LEARNING WITH PYTORCH: A 60 "
+"MINUTE BLITZ "
+"<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:299
+msgid "Defining the model"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:301
+msgid ""
+"We use the simple CNN described in the `PyTorch tutorial "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a"
+"-convolutional-neural-network>`__:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:338
+msgid "Let's continue with the usual training and test functions:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:398
+msgid "Training the model"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:400
+msgid ""
+"We now have all the basic building blocks we need: a dataset, a model, a "
+"training function, and a test function. Let's put them together to train "
+"the model on the dataset of one of our organizations "
+"(``trainloaders[0]``). This simulates the reality of most machine "
+"learning projects today: each organization has their own data and trains "
+"models only on this internal data:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:430
+msgid ""
+"Training the simple CNN on our CIFAR-10 split for 5 epochs should result "
+"in a test set accuracy of about 41%, which is not good, but at the same "
+"time, it doesn't really matter for the purposes of this tutorial. The "
+"intent was just to show a simplistic centralized training pipeline that "
+"sets the stage for what comes next - federated learning!"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:442
+msgid "Step 2: Federated Learning with Flower"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:444
+msgid ""
+"Step 1 demonstrated a simple centralized training pipeline. All data was "
+"in one place (i.e., a single ``trainloader`` and a single ``valloader``)."
+" Next, we'll simulate a situation where we have multiple datasets in "
+"multiple organizations and where we train a model over these "
+"organizations using federated learning."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:456
+msgid "Updating model parameters"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:458
+msgid ""
+"In federated learning, the server sends the global model parameters to "
+"the client, and the client updates the local model with the parameters "
+"received from the server. It then trains the model on the local data "
+"(which changes the model parameters locally) and sends the "
+"updated/changed model parameters back to the server (or, alternatively, "
+"it sends just the gradients back to the server, not the full model "
+"parameters)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:460
+msgid ""
+"We need two helper functions to update the local model with parameters "
+"received from the server and to get the updated model parameters from the"
+" local model: ``set_parameters`` and ``get_parameters``. The following "
+"two functions do just that for the PyTorch model above."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:462
+msgid ""
+"The details of how this works are not really important here (feel free to"
+" consult the PyTorch documentation if you want to learn more). In "
+"essence, we use ``state_dict`` to access PyTorch model parameter tensors."
+" The parameter tensors are then converted to/from a list of NumPy "
+"ndarray's (which Flower knows how to serialize/deserialize):"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:490
+msgid "Implementing a Flower client"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:492
+msgid ""
+"With that out of the way, let's move on to the interesting part. "
+"Federated learning systems consist of a server and multiple clients. In "
+"Flower, we create clients by implementing subclasses of "
+"``flwr.client.Client`` or ``flwr.client.NumPyClient``. We use "
+"``NumPyClient`` in this tutorial because it is easier to implement and "
+"requires us to write less boilerplate."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:494
+msgid ""
+"To implement the Flower client, we create a subclass of "
+"``flwr.client.NumPyClient`` and implement the three methods "
+"``get_parameters``, ``fit``, and ``evaluate``:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:496
+msgid "``get_parameters``: Return the current local model parameters"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:497
+msgid ""
+"``fit``: Receive model parameters from the server, train the model "
+"parameters on the local data, and return the (updated) model parameters "
+"to the server"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:498
+msgid ""
+"``evaluate``: Receive model parameters from the server, evaluate the "
+"model parameters on the local data, and return the evaluation result to "
+"the server"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:500
+msgid ""
+"We mentioned that our clients will use the previously defined PyTorch "
+"components for model training and evaluation. Let's see a simple Flower "
+"client implementation that brings everything together:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:537
+msgid ""
+"Our class ``FlowerClient`` defines how local training/evaluation will be "
+"performed and allows Flower to call the local training/evaluation through"
+" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a"
+" *single client* in our federated learning system. Federated learning "
+"systems have multiple clients (otherwise, there's not much to federate), "
+"so each client will be represented by its own instance of "
+"``FlowerClient``. If we have, for example, three clients in our workload,"
+" then we'd have three instances of ``FlowerClient``. Flower calls "
+"``FlowerClient.fit`` on the respective instance when the server selects a"
+" particular client for training (and ``FlowerClient.evaluate`` for "
+"evaluation)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:541
+msgid "Using the Virtual Client Engine"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:543
+msgid ""
+"In this notebook, we want to simulate a federated learning system with 10"
+" clients on a single machine. This means that the server and all 10 "
+"clients will live on a single machine and share resources such as CPU, "
+"GPU, and memory. Having 10 clients would mean having 10 instances of "
+"``FlowerClient`` in memory. Doing this on a single machine can quickly "
+"exhaust the available memory resources, even if only a subset of these "
+"clients participates in a single round of federated learning."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:545
+msgid ""
+"In addition to the regular capabilities where server and clients run on "
+"multiple machines, Flower, therefore, provides special simulation "
+"capabilities that create ``FlowerClient`` instances only when they are "
+"actually necessary for training or evaluation. To enable the Flower "
+"framework to create clients when necessary, we need to implement a "
+"function called ``client_fn`` that creates a ``FlowerClient`` instance on"
+" demand. Flower calls ``client_fn`` whenever it needs an instance of one "
+"particular client to call ``fit`` or ``evaluate`` (those instances are "
+"usually discarded after use, so they should not keep any local state). "
+"Clients are identified by a client ID, or short ``cid``. The ``cid`` can "
+"be used, for example, to load different local data partitions for "
+"different clients, as can be seen below:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:580
+msgid "Starting the training"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:582
+msgid ""
+"We now have the class ``FlowerClient`` which defines client-side "
+"training/evaluation and ``client_fn`` which allows Flower to create "
+"``FlowerClient`` instances whenever it needs to call ``fit`` or "
+"``evaluate`` on one particular client. The last step is to start the "
+"actual simulation using ``flwr.simulation.start_simulation``."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:584
+msgid ""
+"The function ``start_simulation`` accepts a number of arguments, amongst "
+"them the ``client_fn`` used to create ``FlowerClient`` instances, the "
+"number of clients to simulate (``num_clients``), the number of federated "
+"learning rounds (``num_rounds``), and the strategy. The strategy "
+"encapsulates the federated learning approach/algorithm, for example, "
+"*Federated Averaging* (FedAvg)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:586
+msgid ""
+"Flower has a number of built-in strategies, but we can also use our own "
+"strategy implementations to customize nearly all aspects of the federated"
+" learning approach. For this example, we use the built-in ``FedAvg`` "
+"implementation and customize it using a few basic parameters. The last "
+"step is the actual call to ``start_simulation`` which - you guessed it - "
+"starts the simulation:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628
+msgid "Behind the scenes"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630
+msgid "So how does this work? How does Flower execute this simulation?"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632
+#, python-format
+msgid ""
+"When we call ``start_simulation``, we tell Flower that there are 10 "
+"clients (``num_clients=10``). Flower then goes ahead an asks the "
+"``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should "
+"select 100% of the available clients (``fraction_fit=1.0``), so it goes "
+"ahead and selects 10 random clients (i.e., 100% of 10)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634
+msgid ""
+"Flower then asks the selected 10 clients to train the model. When the "
+"server receives the model parameter updates from the clients, it hands "
+"those updates over to the strategy (*FedAvg*) for aggregation. The "
+"strategy aggregates those updates and returns the new global model, which"
+" then gets used in the next round of federated learning."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:646
+msgid "Where's the accuracy?"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:648
+msgid ""
+"You may have noticed that all metrics except for ``losses_distributed`` "
+"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:650
+msgid ""
+"Flower can automatically aggregate losses returned by individual clients,"
+" but it cannot do the same for metrics in the generic metrics dictionary "
+"(the one with the ``accuracy`` key). Metrics dictionaries can contain "
+"very different kinds of metrics and even key/value pairs that are not "
+"metrics at all, so the framework does not (and can not) know how to "
+"handle these automatically."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:652
+msgid ""
+"As users, we need to tell the framework how to handle/aggregate these "
+"custom metrics, and we do so by passing metric aggregation functions to "
+"the strategy. The strategy will then call these functions whenever it "
+"receives fit or evaluate metrics from clients. The two possible functions"
+" are ``fit_metrics_aggregation_fn`` and "
+"``evaluate_metrics_aggregation_fn``."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:654
+msgid ""
+"Let's create a simple weighted averaging function to aggregate the "
+"``accuracy`` metric we return from ``evaluate``:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:680
+msgid ""
+"The only thing left to do is to tell the strategy to call this function "
+"whenever it receives evaluation metric dictionaries from the clients:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:717
+msgid ""
+"We now have a full system that performs federated training and federated "
+"evaluation. It uses the ``weighted_average`` function to aggregate custom"
+" evaluation metrics and calculates a single ``accuracy`` metric across "
+"all clients on the server side."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:719
+msgid ""
+"The other two categories of metrics (``losses_centralized`` and "
+"``metrics_centralized``) are still empty because they only apply when "
+"centralized evaluation is being used. Part two of the Flower tutorial "
+"will cover centralized evaluation."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:731
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:351
+msgid "Final remarks"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733
+msgid ""
+"Congratulations, you just trained a convolutional neural network, "
+"federated over 10 clients! With that, you understand the basics of "
+"federated learning with Flower. The same approach you've seen can be used"
+" with other machine learning frameworks (not just PyTorch) and tasks (not"
+" just CIFAR-10 images classification), for example NLP with Hugging Face "
+"Transformers or speech with SpeechBrain."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:735
+msgid ""
+"In the next notebook, we're going to cover some more advanced concepts. "
+"Want to customize your strategy? Initialize parameters on the server "
+"side? Or evaluate the aggregated model on the server side? We'll cover "
+"all this and more in the next tutorial."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:753
+msgid ""
+"The `Flower Federated Learning Tutorial - Part 2 "
+"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"strategy-pytorch.html>`__ goes into more depth about strategies and all "
+"the advanced things you can build with them."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9
+msgid "Use a federated learning strategy"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:11
+msgid ""
+"Welcome to the next part of the federated learning tutorial. In previous "
+"parts of this tutorial, we introduced federated learning with PyTorch and"
+" Flower (`part 1 <https://flower.dev/docs/framework/tutorial-get-started-"
+"with-flower-pytorch.html>`__)."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13
+msgid ""
+"In this notebook, we'll begin to customize the federated learning system "
+"we built in the introductory notebook (again, using `Flower "
+"<https://flower.dev/>`__ and `PyTorch <https://pytorch.org/>`__)."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17
+msgid "Let's move beyond FedAvg with Flower strategies!"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:309
+msgid "Strategy customization"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:311
+msgid ""
+"So far, everything should look familiar if you've worked through the "
+"introductory notebook. With that, we're ready to introduce a number of "
+"new features."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323
+msgid "Server-side parameter **initialization**"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325
+msgid ""
+"Flower, by default, initializes the global model by asking one random "
+"client for the initial parameters. In many cases, we want more control "
+"over parameter initialization though. Flower therefore allows you to "
+"directly pass the initial parameters to the Strategy:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:370
+msgid ""
+"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower"
+" from asking one of the clients for the initial parameters. If we look "
+"closely, we can see that the logs do not show any calls to the "
+"``FlowerClient.get_parameters`` method."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:382
+msgid "Starting with a customized strategy"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384
+msgid ""
+"We've seen the function ``start_simulation`` before. It accepts a number "
+"of arguments, amongst them the ``client_fn`` used to create "
+"``FlowerClient`` instances, the number of clients to simulate "
+"``num_clients``, the number of rounds ``num_rounds``, and the strategy."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:386
+msgid ""
+"The strategy encapsulates the federated learning approach/algorithm, for "
+"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different "
+"strategy this time:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424
+msgid "Server-side parameter **evaluation**"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:426
+msgid ""
+"Flower can evaluate the aggregated model on the server-side or on the "
+"client-side. Client-side and server-side evaluation are similar in some "
+"ways, but different in others."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:428
+msgid ""
+"**Centralized Evaluation** (or *server-side evaluation*) is conceptually "
+"simple: it works the same way that evaluation in centralized machine "
+"learning does. If there is a server-side dataset that can be used for "
+"evaluation purposes, then that's great. We can evaluate the newly "
+"aggregated model after each round of training without having to send the "
+"model to clients. We're also fortunate in the sense that our entire "
+"evaluation dataset is available at all times."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:430
+msgid ""
+"**Federated Evaluation** (or *client-side evaluation*) is more complex, "
+"but also more powerful: it doesn't require a centralized dataset and "
+"allows us to evaluate models over a larger set of data, which often "
+"yields more realistic evaluation results. In fact, many scenarios require"
+" us to use **Federated Evaluation** if we want to get representative "
+"evaluation results at all. But this power comes at a cost: once we start "
+"to evaluate on the client side, we should be aware that our evaluation "
+"dataset can change over consecutive rounds of learning if those clients "
+"are not always available. Moreover, the dataset held by each client can "
+"also change over consecutive rounds. This can lead to evaluation results "
+"that are not stable, so even if we would not change the model, we'd see "
+"our evaluation results fluctuate over consecutive rounds."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:433
+msgid ""
+"We've seen how federated evaluation works on the client side (i.e., by "
+"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see "
+"how we can evaluate aggregated model parameters on the server-side:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:490
+msgid "Sending/receiving arbitrary values to/from clients"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492
+msgid ""
+"In some situations, we want to configure client-side execution (training,"
+" evaluation) from the server-side. One example for that is the server "
+"asking the clients to train for a certain number of local epochs. Flower "
+"provides a way to send configuration values from the server to the "
+"clients using a dictionary. Let's look at an example where the clients "
+"receive values from the server through the ``config`` parameter in "
+"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` "
+"method receives the configuration dictionary through the ``config`` "
+"parameter and can then read values from this dictionary. In this example,"
+" it reads ``server_round`` and ``local_epochs`` and uses those values to "
+"improve the logging and configure the number of local training epochs:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:546
+msgid ""
+"So how can we send this config dictionary from server to clients? The "
+"built-in Flower Strategies provide way to do this, and it works similarly"
+" to the way server-side evaluation works. We provide a function to the "
+"strategy, and the strategy calls this function for every round of "
+"federated learning:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:576
+msgid ""
+"Next, we'll just pass this function to the FedAvg strategy before "
+"starting the simulation:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:613
+msgid ""
+"As we can see, the client logs now include the current round of federated"
+" learning (which they read from the ``config`` dictionary). We can also "
+"configure local training to run for one epoch during the first and second"
+" round of federated learning, and then for two epochs during the third "
+"round."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:615
+msgid ""
+"Clients can also return arbitrary values to the server. To do so, they "
+"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and "
+"used this concept throughout this notebook without mentioning it "
+"explicitly: our ``FlowerClient`` returns a dictionary containing a custom"
+" key/value pair as the third return value in ``evaluate``."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:627
+msgid "Scaling federated learning"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:629
+msgid ""
+"As a last step in this notebook, let's see how we can use Flower to "
+"experiment with a large number of clients."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:651
+#, python-format
+msgid ""
+"We now have 1000 partitions, each holding 45 training and 5 validation "
+"examples. Given that the number of training examples on each client is "
+"quite small, we should probably train the model a bit longer, so we "
+"configure the clients to perform 3 local training epochs. We should also "
+"adjust the fraction of clients selected for training during each round "
+"(we don't want all 1000 clients participating in every round), so we "
+"adjust ``fraction_fit`` to ``0.05``, which means that only 5% of "
+"available clients (so 50 clients) will be selected for training each "
+"round:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:699
+msgid ""
+"In this notebook, we've seen how we can gradually enhance our system by "
+"customizing the strategy, initializing parameters on the server side, "
+"choosing a different strategy, and evaluating models on the server-side. "
+"That's quite a bit of flexibility with so little code, right?"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:701
+msgid ""
+"In the later sections, we've seen how we can communicate arbitrary values"
+" between server and clients to fully customize client-side execution. "
+"With that capability, we built a large-scale Federated Learning "
+"simulation using the Flower Virtual Client Engine and ran an experiment "
+"involving 1000 clients in the same workload - all in a Jupyter Notebook!"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719
+msgid ""
+"The `Flower Federated Learning Tutorial - Part 3 "
+"<https://flower.dev/docs/framework/tutorial-build-a-strategy-from-"
+"scratch-pytorch.html>`__ shows how to build a fully custom ``Strategy`` "
+"from scratch."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:9
+msgid "What is Federated Learning?"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:13
+msgid ""
+"In this tutorial, you will learn what federated learning is, build your "
+"first system in Flower, and gradually extend it. If you work through all "
+"parts of the tutorial, you will be able to build advanced federated "
+"learning systems that approach the current state of the art in the field."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:15
+msgid ""
+"🧑‍🏫 This tutorial starts at zero and expects no familiarity with "
+"federated learning. Only a basic understanding of data science and Python"
+" programming is assumed."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:17
+msgid ""
+"`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
+"the open-source Flower community on Slack to connect, ask questions, and "
+"get help: `Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to "
+"hear from you in the ``#introductions`` channel! And if anything is "
+"unclear, head over to the ``#questions`` channel."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:19
+msgid "Let's get started!"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:31
+msgid "Classic machine learning"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:33
+msgid ""
+"Before we begin to discuss federated learning, let us quickly recap how "
+"most machine learning works today."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:35
+msgid ""
+"In machine learning, we have a model, and we have data. The model could "
+"be a neural network (as depicted here), or something else, like classical"
+" linear regression."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:41
+msgid "|e1dd4b4129b040bea23a894266227080|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:109
+msgid "Model and data"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:47
+msgid ""
+"We train the model using the data to perform a useful task. A task could "
+"be to detect objects in images, transcribe an audio recording, or play a "
+"game like Go."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:53
+msgid "|c0d4cc6a442948dca8da40d2440068d9|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:111
+msgid "Train model using data"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:59
+msgid ""
+"Now, in practice, the training data we work with doesn't originate on the"
+" machine we train the model on. It gets created somewhere else."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:61
+msgid ""
+"It originates on a smartphone by the user interacting with an app, a car "
+"collecting sensor data, a laptop receiving input via the keyboard, or a "
+"smart speaker listening to someone trying to sing a song."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:67
+msgid "|174e1e4fa1f149a19bfbc8bc1126f46a|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:113
+msgid "Data on a phone"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:73
+msgid ""
+"What's also important to mention, this \"somewhere else\" is usually not "
+"just one place, it's many places. It could be several devices all running"
+" the same app. But it could also be several organizations, all generating"
+" data for the same task."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:79
+msgid "|4e021a3dc08249d2a89daa3ab03c2714|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:115
+msgid "Data is on many devices"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:85
+msgid ""
+"So to use machine learning, or any kind of data analysis, the approach "
+"that has been used in the past was to collect all data on a central "
+"server. This server can be somewhere in a data center, or somewhere in "
+"the cloud."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:91
+msgid "|e74a1d5ce7eb49688651f2167a59065b|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:117
+msgid "Central data collection"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:97
+msgid ""
+"Once all the data is collected in one place, we can finally use machine "
+"learning algorithms to train our model on the data. This is the machine "
+"learning approach that we've basically always relied on."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:103
+msgid "|eb29ec4c7aef4e93976795ed72df647e|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:119
+msgid "Central model training"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:130
+msgid "Challenges of classical machine learning"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:132
+msgid ""
+"The classic machine learning approach we've just seen can be used in some"
+" cases. Great examples include categorizing holiday photos, or analyzing "
+"web traffic. Cases, where all the data is naturally available on a "
+"centralized server."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:138
+msgid "|c2f699d8ac484f5081721a6f1511f70d|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:173
+msgid "Centralized possible"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:144
+msgid ""
+"But the approach can not be used in many other cases. Cases, where the "
+"data is not available on a centralized server, or cases where the data "
+"available on one server is not enough to train a good model."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:150
+msgid "|cf42accdacbf4e5eb4fa0503108ba7a7|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:175
+msgid "Centralized impossible"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:156
+msgid ""
+"There are many reasons why the classic centralized machine learning "
+"approach does not work for a large number of highly important real-world "
+"use cases. Those reasons include:"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:158
+msgid ""
+"**Regulations**: GDPR (Europe), CCPA (California), PIPEDA (Canada), LGPD "
+"(Brazil), PDPL (Argentina), KVKK (Turkey), POPI (South Africa), FSS "
+"(Russia), CDPR (China), PDPB (India), PIPA (Korea), APPI (Japan), PDP "
+"(Indonesia), PDPA (Singapore), APP (Australia), and other regulations "
+"protect sensitive data from being moved. In fact, those regulations "
+"sometimes even prevent single organizations from combining their own "
+"users' data for artificial intelligence training because those users live"
+" in different parts of the world, and their data is governed by different"
+" data protection regulations."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:160
+msgid ""
+"**User preference**: In addition to regulation, there are use cases where"
+" users just expect that no data leaves their device, ever. If you type "
+"your passwords and credit card info into the digital keyboard of your "
+"phone, you don't expect those passwords to end up on the server of the "
+"company that developed that keyboard, do you? In fact, that use case was "
+"the reason federated learning was invented in the first place."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:161
+msgid ""
+"**Data volume**: Some sensors, like cameras, produce such a high data "
+"volume that it is neither feasible nor economic to collect all the data "
+"(due to, for example, bandwidth or communication efficiency). Think about"
+" a national rail service with hundreds of train stations across the "
+"country. If each of these train stations is outfitted with a number of "
+"security cameras, the volume of raw on-device data they produce requires "
+"incredibly powerful and exceedingly expensive infrastructure to process "
+"and store. And most of the data isn't even useful."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:164
+msgid "Examples where centralized machine learning does not work include:"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:166
+msgid ""
+"Sensitive healthcare records from multiple hospitals to train cancer "
+"detection models"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:167
+msgid ""
+"Financial information from different organizations to detect financial "
+"fraud"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:168
+msgid "Location data from your electric car to make better range prediction"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:169
+msgid "End-to-end encrypted messages to train better auto-complete models"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:171
+msgid ""
+"The popularity of privacy-enhancing systems like the `Brave "
+"<https://brave.com/>`__ browser or the `Signal <https://signal.org/>`__ "
+"messenger shows that users care about privacy. In fact, they choose the "
+"privacy-enhancing version over other alternatives, if such an alernative "
+"exists. But what can we do to apply machine learning and data science to "
+"these cases to utilize private data? After all, these are all areas that "
+"would benefit significantly from recent advances in AI."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:186
+msgid "Federated learning"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:188
+msgid ""
+"Federated learning simply reverses this approach. It enables machine "
+"learning on distributed data by moving the training to the data, instead "
+"of moving the data to the training. Here's the single-sentence "
+"explanation:"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:190
+msgid "Central machine learning: move the data to the computation"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:191
+msgid "Federated (machine) learning: move the computation to the data"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:193
+msgid ""
+"By doing so, it enables us to use machine learning (and other data "
+"science approaches) in areas where it wasn't possible before. We can now "
+"train excellent medical AI models by enabling different hospitals to work"
+" together. We can solve financial fraud by training AI models on the data"
+" of different financial institutions. We can build novel privacy-"
+"enhancing applications (such as secure messaging) that have better built-"
+"in AI than their non-privacy-enhancing alternatives. And those are just a"
+" few of the examples that come to mind. As we deploy federated learning, "
+"we discover more and more areas that can suddenly be reinvented because "
+"they now have access to vast amounts of previously inaccessible data."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:196
+msgid ""
+"So how does federated learning work, exactly? Let's start with an "
+"intuitive explanation."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:199
+msgid "Federated learning in five steps"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:202
+msgid "Step 0: Initialize global model"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:204
+msgid ""
+"We start by initializing the model on the server. This is exactly the "
+"same in classic centralized learning: we initialize the model parameters,"
+" either randomly or from a previously saved checkpoint."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:210
+msgid "|5ec8356bc2564fa09178b1ceed5beccc|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:307
+msgid "Initialize global model"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:217
+msgid ""
+"Step 1: Send model to a number of connected organizations/devices (client"
+" nodes)"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:219
+msgid ""
+"Next, we send the parameters of the global model to the connected client "
+"nodes (think: edge devices like smartphones or servers belonging to "
+"organizations). This is to ensure that each participating node starts "
+"their local training using the same model parameters. We often use only a"
+" few of the connected nodes instead of all nodes. The reason for this is "
+"that selecting more and more client nodes has diminishing returns."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:225
+msgid "|7c9329e97bd0430bad335ab605a897a7|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:309
+msgid "Send global model"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:232
+msgid ""
+"Step 2: Train model locally on the data of each organization/device "
+"(client node)"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:234
+msgid ""
+"Now that all (selected) client nodes have the latest version of the "
+"global model parameters, they start the local training. They use their "
+"own local dataset to train their own local model. They don't train the "
+"model until full convergence, but they only train for a little while. "
+"This could be as little as one epoch on the local data, or even just a "
+"few steps (mini-batches)."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:240
+msgid "|88002bbce1094ba1a83c9151df18f707|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:311
+msgid "Train on local data"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:247
+msgid "Step 3: Return model updates back to the server"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:249
+msgid ""
+"After local training, each client node has a slightly different version "
+"of the model parameters they originally received. The parameters are all "
+"different because each client node has different examples in its local "
+"dataset. The client nodes then send those model updates back to the "
+"server. The model updates they send can either be the full model "
+"parameters or just the gradients that were accumulated during local "
+"training."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:255
+msgid "|391766aee87c482c834c93f7c22225e2|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:313
+msgid "Send model updates"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:262
+msgid "Step 4: Aggregate model updates into a new global model"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:264
+msgid ""
+"The server receives model updates from the selected client nodes. If it "
+"selected 100 client nodes, it now has 100 slightly different versions of "
+"the original global model, each trained on the local data of one client. "
+"But didn't we want to have one model that contains the learnings from the"
+" data of all 100 client nodes?"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:266
+msgid ""
+"In order to get one single model, we have to combine all the model "
+"updates we received from the client nodes. This process is called "
+"*aggregation*, and there are many different ways to do it. The most basic"
+" way to do it is called *Federated Averaging* (`McMahan et al., 2016 "
+"<https://arxiv.org/abs/1602.05629>`__), often abbreviated as *FedAvg*. "
+"*FedAvg* takes the 100 model updates and, as the name suggests, averages "
+"them. To be more precise, it takes the *weighted average* of the model "
+"updates, weighted by the number of examples each client used for "
+"training. The weighting is important to make sure that each data example "
+"has the same \"influence\" on the resulting global model. If one client "
+"has 10 examples, and another client has 100 examples, then - without "
+"weighting - each of the 10 examples would influence the global model ten "
+"times as much as each of the 100 examples."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:273
+msgid "|93b9a15bd27f4e91b40f642c253dfaac|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:315
+msgid "Aggregate model updates"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:280
+msgid "Step 5: Repeat steps 1 to 4 until the model converges"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:282
+msgid ""
+"Steps 1 to 4 are what we call a single round of federated learning. The "
+"global model parameters get sent to the participating client nodes (step "
+"1), the client nodes train on their local data (step 2), they send their "
+"updated models to the server (step 3), and the server then aggregates the"
+" model updates to get a new version of the global model (step 4)."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:284
+msgid ""
+"During a single round, each client node that participates in that "
+"iteration only trains for a little while. This means that after the "
+"aggregation step (step 4), we have a model that has been trained on all "
+"the data of all participating client nodes, but only for a little while. "
+"We then have to repeat this training process over and over again to "
+"eventually arrive at a fully trained model that performs well across the "
+"data of all client nodes."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:289
+msgid ""
+"Congratulations, you now understand the basics of federated learning. "
+"There's a lot more to discuss, of course, but that was federated learning"
+" in a nutshell. In later parts of this tutorial, we will go into more "
+"detail. Interesting questions include: How can we select the best client "
+"nodes that should participate in the next round? What's the best way to "
+"aggregate model updates? How can we handle failing client nodes "
+"(stragglers)?"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:294
+msgid ""
+"Just like we can train a model on the decentralized data of different "
+"client nodes, we can also evaluate the model on that data to receive "
+"valuable metrics. This is called federated evaluation, sometimes "
+"abbreviated as FE. In fact, federated evaluation is an integral part of "
+"most federated learning systems."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:297
+msgid "Federated analytics"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:299
+msgid ""
+"In many cases, machine learning isn't necessary to derive value from "
+"data. Data analysis can yield valuable insights, but again, there's often"
+" not enough data to get a clear answer. What's the average age at which "
+"people develop a certain type of health condition? Federated analytics "
+"enables such queries over multiple client nodes. It is usually used in "
+"conjunction with other privacy-enhancing technologies like secure "
+"aggregation to prevent the server from seeing the results submitted by "
+"individual client nodes."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:303
+msgid "Differential Privacy"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:305
+msgid ""
+"Differential privacy (DP) is often mentioned in the context of Federated "
+"Learning. It is a privacy-preserving method used when analyzing and "
+"sharing statistical data, ensuring the privacy of individual "
+"participants. DP achieves this by adding statistical noise to the model "
+"updates, ensuring any individual participants’ information cannot be "
+"distinguished or re-identified. This technique can be considered an "
+"optimization that provides a quantifiable privacy protection measure."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:326
+msgid "Flower"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:328
+msgid ""
+"Federated learning, federated evaluation, and federated analytics require"
+" infrastructure to move machine learning models back and forth, train and"
+" evaluate them on local data, and then aggregate the updated models. "
+"Flower provides the infrastructure to do exactly that in an easy, "
+"scalable, and secure way. In short, Flower presents a unified approach to"
+" federated learning, analytics, and evaluation. It allows the user to "
+"federate any workload, any ML framework, and any programming language."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:334
+msgid "|a23d9638f96342ef9d25209951e2d564|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:340
+msgid ""
+"Flower federated learning server and client nodes (car, scooter, personal"
+" computer, roomba, and phone)"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:353
+msgid ""
+"Congratulations, you just learned the basics of federated learning and "
+"how it relates to the classic (centralized) machine learning!"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:355
+msgid ""
+"In the next part of this tutorial, we are going to build a first "
+"federated learning system with Flower."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:373
+msgid ""
+"The `Flower Federated Learning Tutorial - Part 1 "
+"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"pytorch.html>`__ shows how to build a simple federated learning system "
+"with PyTorch and Flower."
+msgstr ""
+
diff --git a/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po b/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po
new file mode 100644
index 000000000000..98f300b872e3
--- /dev/null
+++ b/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po
@@ -0,0 +1,37 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022 Flower Labs GmbH
+# This file is distributed under the same license as the Flower package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: Flower main\n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2023-11-23 18:31+0100\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language: zh_Hans\n"
+"Language-Team: zh_Hans <LL@li.org>\n"
+"Plural-Forms: nplurals=1; plural=0;\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.13.1\n"
+
+#: ../../source/_templates/base.html:18
+msgid "About these documents"
+msgstr ""
+
+#: ../../source/_templates/base.html:21
+msgid "Index"
+msgstr ""
+
+#: ../../source/_templates/base.html:24
+msgid "Search"
+msgstr ""
+
+#: ../../source/_templates/base.html:27
+msgid "Copyright"
+msgstr ""
+

From 01fdba45ded2a7f76a1d7a75496100ecd1217609 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Thu, 23 Nov 2023 21:45:55 +0100
Subject: [PATCH 44/64] Fix images in example docs (#2630)

---
 dev/update-examples.sh                  |   9 +++++++--
 examples/doc/source/_static/.gitignore  |   5 +++++
 examples/doc/source/_static/diagram.png | Bin 358110 -> 0 bytes
 3 files changed, 12 insertions(+), 2 deletions(-)
 create mode 100644 examples/doc/source/_static/.gitignore
 delete mode 100644 examples/doc/source/_static/diagram.png

diff --git a/dev/update-examples.sh b/dev/update-examples.sh
index 83dff474c855..c802e21503b7 100755
--- a/dev/update-examples.sh
+++ b/dev/update-examples.sh
@@ -22,9 +22,14 @@ cd examples/
 for d in $(printf '%s\n' */ | sort -V); do
   example=${d%/}
   # For each example, copy the README into the source of the Example docs
-  [[ $example = doc ]] || cp $example/README.md $ROOT/examples/doc/source/$example.md 2>&1 >/dev/null
+  [[ $example != doc ]] && cp $example/README.md $ROOT/examples/doc/source/$example.md 2>&1 >/dev/null
+  # For each example, copy all images of the _static folder into the examples
+  # docs static folder
+  [[ $example != doc ]] && [ -d "$example/_static" ] && {
+    cp $example/_static/**.{jpg,png,jpeg} $ROOT/examples/doc/source/_static/ 2>/dev/null || true
+  }
   # For each example, insert the name of the example into the index file
-  [[ $example = doc ]] || (echo $INSERT_LINE; echo a; echo $example; echo .; echo wq) | ed $INDEX 2>&1 >/dev/null
+  [[ $example != doc ]] && (echo $INSERT_LINE; echo a; echo $example; echo .; echo wq) | ed $INDEX 2>&1 >/dev/null
 done
 
 echo "\`\`\`" >> $INDEX
diff --git a/examples/doc/source/_static/.gitignore b/examples/doc/source/_static/.gitignore
new file mode 100644
index 000000000000..c2412a5912cc
--- /dev/null
+++ b/examples/doc/source/_static/.gitignore
@@ -0,0 +1,5 @@
+*
+!custom.css
+!favicon.ico
+!flower-logo.png
+!tmux_jtop_view.gif
diff --git a/examples/doc/source/_static/diagram.png b/examples/doc/source/_static/diagram.png
deleted file mode 100644
index 66d8855c859f33581bbb428823ef0e3c3fe3e340..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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From 7259a0e7c4d7c553e84c5cf2af4d607b4de67a4b Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Fri, 24 Nov 2023 20:47:59 +0000
Subject: [PATCH 45/64] Add youtube link (#2634)

Co-authored-by: yan-gao-GY <yan@flower.dev>
---
 doc/source/tutorial-quickstart-xgboost.rst | 35 ++++++++++++----------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/doc/source/tutorial-quickstart-xgboost.rst b/doc/source/tutorial-quickstart-xgboost.rst
index 111920d5602b..7eb58da7f2f6 100644
--- a/doc/source/tutorial-quickstart-xgboost.rst
+++ b/doc/source/tutorial-quickstart-xgboost.rst
@@ -2,13 +2,16 @@
 
 
 Quickstart XGBoost
-==================
+=====================
 
 .. meta::
    :description: Check out this Federated Learning quickstart tutorial for using Flower with XGBoost to train classification models on trees.
 
+..  youtube:: AY1vpXUpesc
+   :width: 100%
+
 Federated XGBoost
--------------
+-------------------
 
 EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient implementation of gradient-boosted decision tree (**GBDT**), that maximises the computational boundaries for boosted tree methods.
 It's primarily designed to enhance both the performance and computational speed of machine learning models.
@@ -17,7 +20,7 @@ In XGBoost, trees are constructed concurrently, unlike the sequential approach t
 Often, for tabular data on medium-sized datasets with fewer than 10k training examples, XGBoost surpasses the results of deep learning techniques.
 
 Why federated XGBoost?
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Indeed, as the demand for data privacy and decentralized learning grows, there's an increasing requirement to implement federated XGBoost systems for specialised applications, like survival analysis and financial fraud detection.
 
@@ -31,7 +34,7 @@ and then we dive into a more complex example (`full code xgboost-comprehensive <
 
 
 Environment Setup
--------------
+--------------------
 
 First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_.
 
@@ -49,7 +52,7 @@ Since we want to use :code:`xgboost` package to build up XGBoost trees, let's go
 
 
 Flower Client
--------------
+-----------------
 
 *Clients* are responsible for generating individual weight-updates for the model based on their local datasets.
 Now that we have all our dependencies installed, let's run a simple distributed training with two clients and one server.
@@ -81,7 +84,7 @@ In a file called :code:`client.py`, import xgboost, Flower, Flower Datasets and
     from flwr_datasets.partitioner import IidPartitioner
 
 Dataset partition and hyper-parameter selection
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Prior to local training, we require loading the HIGGS dataset from Flower Datasets and conduct data partitioning for FL:
 
@@ -175,7 +178,7 @@ We use AUC as evaluation metric.
 
 
 Flower client definition for XGBoost
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 After loading the dataset we define the Flower client.
 We follow the general rule to define :code:`XgbClient` class inherited from :code:`fl.client.Client`.
@@ -303,7 +306,7 @@ clients running on different machines, all that needs to change is the
 
 
 Flower Server
--------------
+------------------
 
 These updates are then sent to the *server* which will aggregate them to produce a better model.
 Finally, the *server* sends this improved version of the model back to each *client* to finish a complete FL round.
@@ -348,7 +351,7 @@ Then, we start the server:
     )
 
 Tree-based bagging aggregation
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 You must be curious about how bagging aggregation works. Let's look into the details.
 
@@ -517,7 +520,7 @@ followed by the serialisation, and sending back to each client.
 
 
 Launch Federated XGBoost!
----------------------------
+-------------------------------
 
 With both client and server ready, we can now run everything and see federated
 learning in action. FL systems usually have a server and multiple clients. We
@@ -588,7 +591,7 @@ The full `source code <https://github.com/adap/flower/blob/main/examples/xgboost
 
 
 Comprehensive Federated XGBoost
----------------------------
+-----------------------------------
 
 Now that you have known how federated XGBoost work with Flower, it's time to run some more comprehensive experiments by customising the experimental settings.
 In the xgboost-comprehensive example (`full code <https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive>`_),
@@ -596,7 +599,7 @@ we provide more options to define various experimental setups, including data pa
 Let's take a look!
 
 Customised data partitioning
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In :code:`dataset.py`, we have a function :code:`instantiate_partitioner` to instantiate the data partitioner
 based on the given :code:`num_partitions` and :code:`partitioner_type`.
@@ -629,7 +632,7 @@ Currently, we provide four supported partitioner type to simulate the uniformity
 
 
 Customised centralised/distributed evaluation
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 To facilitate centralised evaluation, we define a function in :code:`server.py`:
 
@@ -670,7 +673,7 @@ As for distributed evaluation on the clients, it's same as the quick-start examp
 overriding the :code:`evaluate()` method insides the :code:`XgbClient` class in :code:`client.py`.
 
 Arguments parser
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~
 
 In :code:`utils.py`, we define the arguments parsers for clients and server, allowing users to specify different experimental settings.
 Let's first see the sever side:
@@ -761,7 +764,7 @@ This defines various options for client data partitioning.
 Besides, clients also have a option to conduct evaluation on centralised test set by setting :code:`--centralised-eval`.
 
 Example commands
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 To run a centralised evaluated experiment on 5 clients with exponential distribution for 50 rounds,
 we first start the server as below:
@@ -776,4 +779,4 @@ Then, on each client terminal, we start the clients:
 
     $ python3 clients.py --num-partitions=5 --partitioner-type=exponential --node-id=NODE_ID
 
-The full `source code <https://github.com/adap/flower/blob/main/examples/xgboost-comprehensive/>`_ for this comprehensive example can be found in :code:`examples/xgboost-comprehensive`.
+The full `code <https://github.com/adap/flower/blob/main/examples/xgboost-comprehensive/>`_ for this comprehensive example can be found in :code:`examples/xgboost-comprehensive`.

From 8c21626c79fe8c2f134e95b27a4ded778a398b89 Mon Sep 17 00:00:00 2001
From: "Weblate (bot)" <hosted@weblate.org>
Date: Mon, 27 Nov 2023 13:02:29 +0100
Subject: [PATCH 46/64] Translated using Weblate (Chinese (Simplified)) (#2637)

Co-authored-by: Yan Gao <y.gaogy@gmail.com>
---
 .../zh_Hans/LC_MESSAGES/framework-docs.po     | 28 +++++++++----------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index f077264b4aab..87af28422d56 100644
--- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -3,59 +3,60 @@
 # This file is distributed under the same license as the Flower package.
 # FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
 #
-#, fuzzy
 msgid ""
 msgstr ""
 "Project-Id-Version: Flower main\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2023-11-23 18:31+0100\n"
-"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
-"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"PO-Revision-Date: 2023-11-25 19:00+0000\n"
+"Last-Translator: Yan Gao <y.gaogy@gmail.com>\n"
+"Language-Team: Chinese (Simplified) <https://hosted.weblate.org/projects/"
+"flower-docs/framework/zh_Hans/>\n"
 "Language: zh_Hans\n"
-"Language-Team: zh_Hans <LL@li.org>\n"
-"Plural-Forms: nplurals=1; plural=0;\n"
 "MIME-Version: 1.0\n"
 "Content-Type: text/plain; charset=utf-8\n"
 "Content-Transfer-Encoding: 8bit\n"
+"Plural-Forms: nplurals=1; plural=0;\n"
+"X-Generator: Weblate 5.2.1-rc\n"
 "Generated-By: Babel 2.13.1\n"
 
 #: ../../source/contributor-explanation-architecture.rst:2
 msgid "Flower Architecture"
-msgstr ""
+msgstr "Flower的架构"
 
 #: ../../source/contributor-explanation-architecture.rst:5
 msgid "Edge Client Engine"
-msgstr ""
+msgstr "边缘客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:7
 msgid ""
 "`Flower <https://flower.dev>`_ core framework architecture with Edge "
 "Client Engine"
-msgstr ""
+msgstr "具有边缘客户端引擎的`Flower <https://flower.dev>`核心架构"
 
 #: ../../source/contributor-explanation-architecture.rst:13
 msgid "Virtual Client Engine"
-msgstr ""
+msgstr "虚拟客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:15
 msgid ""
 "`Flower <https://flower.dev>`_ core framework architecture with Virtual "
 "Client Engine"
-msgstr ""
+msgstr "具有虚拟客户端引擎的`Flower <https://flower.dev>`核心架构"
 
 #: ../../source/contributor-explanation-architecture.rst:21
 msgid "Virtual Client Engine and Edge Client Engine in the same workload"
-msgstr ""
+msgstr "可同步进行的虚拟客户端引擎和边缘客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:23
 msgid ""
 "`Flower <https://flower.dev>`_ core framework architecture with both "
 "Virtual Client Engine and Edge Client Engine"
-msgstr ""
+msgstr "具有虚拟客户端引擎和边缘客户端引擎的`Flower <https://flower.dev>`核心架构"
 
 #: ../../source/contributor-how-create-new-messages.rst:2
 msgid "Creating New Messages"
-msgstr ""
+msgstr "创建新信息"
 
 #: ../../source/contributor-how-create-new-messages.rst:4
 msgid ""
@@ -13605,4 +13606,3 @@ msgid ""
 "pytorch.html>`__ shows how to build a simple federated learning system "
 "with PyTorch and Flower."
 msgstr ""
-

From 37aad44fe47810afef1201791a2b1477eea76cc5 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Mon, 27 Nov 2023 15:23:27 +0100
Subject: [PATCH 47/64] Add Vertical FL example (#2598)

---
 doc/source/ref-changelog.md                   |   4 +
 examples/vertical-fl/.gitignore               |   2 +
 examples/vertical-fl/README.md                | 510 ++++++++++
 examples/vertical-fl/_static/data/train.csv   | 892 ++++++++++++++++++
 examples/vertical-fl/_static/hfl.jpg          | Bin 0 -> 778069 bytes
 examples/vertical-fl/_static/vfl-accuracy.png | Bin 0 -> 33948 bytes
 examples/vertical-fl/_static/vfl.jpg          | Bin 0 -> 667967 bytes
 examples/vertical-fl/client.py                |  27 +
 examples/vertical-fl/plot.py                  |   8 +
 examples/vertical-fl/pyproject.toml           |  18 +
 examples/vertical-fl/requirements.txt         |   6 +
 examples/vertical-fl/simulation.py            |  22 +
 examples/vertical-fl/strategy.py              | 106 +++
 examples/vertical-fl/task.py                  |  90 ++
 14 files changed, 1685 insertions(+)
 create mode 100644 examples/vertical-fl/.gitignore
 create mode 100644 examples/vertical-fl/README.md
 create mode 100644 examples/vertical-fl/_static/data/train.csv
 create mode 100644 examples/vertical-fl/_static/hfl.jpg
 create mode 100644 examples/vertical-fl/_static/vfl-accuracy.png
 create mode 100644 examples/vertical-fl/_static/vfl.jpg
 create mode 100644 examples/vertical-fl/client.py
 create mode 100644 examples/vertical-fl/plot.py
 create mode 100644 examples/vertical-fl/pyproject.toml
 create mode 100644 examples/vertical-fl/requirements.txt
 create mode 100644 examples/vertical-fl/simulation.py
 create mode 100644 examples/vertical-fl/strategy.py
 create mode 100644 examples/vertical-fl/task.py

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index dd8a6e225401..202d70b24ceb 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -6,6 +6,10 @@
 
 - **Add experimental support for Python 3.12** ([#2565](https://github.com/adap/flower/pull/2565))
 
+- **Add Vertical FL example** ([#2598](https://github.com/adap/flower/pull/2598))
+
+  We had many questions about Vertical Federated Learning using Flower, so we decided to add an simple example for it on the [Titanic dataset](https://www.kaggle.com/competitions/titanic/data) alongside a tutorial (in the README).
+
 - **Support custom** `ClientManager` **in** `start_driver()` ([#2292](https://github.com/adap/flower/pull/2292))
 
 - **Update REST API to support create and delete nodes** ([#2283](https://github.com/adap/flower/pull/2283))
diff --git a/examples/vertical-fl/.gitignore b/examples/vertical-fl/.gitignore
new file mode 100644
index 000000000000..64af4779185a
--- /dev/null
+++ b/examples/vertical-fl/.gitignore
@@ -0,0 +1,2 @@
+_static/results
+!_static/data/train.csv
diff --git a/examples/vertical-fl/README.md b/examples/vertical-fl/README.md
new file mode 100644
index 000000000000..d5ab0ab9c30d
--- /dev/null
+++ b/examples/vertical-fl/README.md
@@ -0,0 +1,510 @@
+# Vertical Federated Learning example
+
+This example will showcase how you can perform Vertical Federated Learning using
+Flower. We'll be using the [Titanic dataset](https://www.kaggle.com/competitions/titanic/data)
+to train simple regression models for binary classification. We will go into
+more details below, but the main idea of Vertical Federated Learning is that
+each client is holding different feature sets of the same dataset and that the
+server is holding the labels of this dataset.
+
+## Project Setup
+
+Start by cloning the example project. We prepared a single-line command that you
+can copy into your shell which will checkout the example for you:
+
+```shell
+git clone --depth=1 https://github.com/adap/flower.git _tmp && mv _tmp/examples/vertical-fl . && rm -rf _tmp && cd vertical-fl
+```
+
+This will create a new directory called `vertical-fl` containing the
+following files:
+
+```shell
+-- pyproject.toml
+-- requirements.txt
+-- _static/data/train.csv
+-- client.py
+-- plot.py
+-- simulation.py
+-- strategy.py
+-- task.py
+-- README.md
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `torch` and `flwr`) are defined in
+`pyproject.toml` and `requirements.txt`. We recommend
+[Poetry](https://python-poetry.org/docs/) to install those dependencies and
+manage your virtual environment ([Poetry
+installation](https://python-poetry.org/docs/#installation)) or
+[pip](https://pip.pypa.io/en/latest/development/), but feel free to use a
+different way of installing dependencies and managing virtual environments if
+you have other preferences.
+
+#### Poetry
+
+```shell
+poetry install
+poetry shell
+```
+
+Poetry will install all your dependencies in a newly created virtual
+environment. To verify that everything works correctly you can run the following
+command:
+
+```shell
+poetry run python3 -c "import flwr"
+```
+
+If you don't see any errors you're good to go!
+
+#### pip
+
+Write the command below in your terminal to install the dependencies according
+to the configuration file requirements.txt.
+
+```shell
+pip install -r requirements.txt
+```
+
+## Usage
+
+Once everything is installed, you can just run:
+
+```shell
+poetry run python3 simulation.py
+```
+
+for `poetry`, otherwise just run:
+
+```shell
+python3 simulation.py
+```
+
+This will start the Vertical FL training for 1000 rounds with 3 clients.
+Eventhough the number of rounds is quite high, this should only take a few
+seconds to run as the model is very small.
+
+## Explanations
+
+### Vertical FL vs Horizontal FL
+
+|                       | Horizontal Federated Learning (HFL or just FL)                                                                                                                                                           | Vertical Federated Learning (VFL)                                                                                                                                                                                                                                                                                                                                        |
+| --------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| Data Distribution     | Clients have different data instances but share the same feature space.  Think of different hospitals having different patients' data (samples)  but recording the same types of information (features). | Each client holds different features for the same instances.  Imagine different institutions holding various tests or  measurements for the same group of patients.                                                                                                                                                                                                      |
+| Model Training        | Each client trains a model on their local data,  which contains all the feature columns for its samples.                                                                                                 | Clients train models on their respective features without  having access to the complete feature set.  Each model only sees a vertical slice of the data (hence the name 'Vertical').                                                                                                                                                                                    |
+| Aggregation           | The server aggregates these local models by averaging  the parameters or gradients to update a global model.                                                                                             | The server aggregates the updates such as gradients or parameters,  which are then used to update the global model.  However, since each client sees only a part of the features,  the server typically has a more complex role,  sometimes needing to coordinate more sophisticated aggregation strategies  that may involve secure multi-party computation techniques. |
+| Privacy Consideration | The raw data stays on the client's side, only model updates are shared,  which helps in maintaining privacy.                                                                                             | VFL is designed to ensure that no participant can access  the complete feature set of any sample,  thereby preserving the privacy of data.                                                                                                                                                                                                                               |
+
+|               HFL               |               VFL               |
+| :-----------------------------: | :-----------------------------: |
+| ![HFL diagram](_static/hfl.jpg) | ![VFL diagram](_static/vfl.jpg) |
+
+Those diagrams illustrate HFL vs VFL using a simplified version of what we will be building in this example. Note that on the VFL side, the server holds the labels (the `Survived` column) and will be the only one capable of performing evaluation.
+
+### Data
+
+#### About
+
+The Titanic Survival dataset is a popular dataset used to predict passenger survival on
+the Titanic based on various features.
+
+You can see an exhaustive list of the features over on [Kaggle](https://www.kaggle.com/competitions/titanic/data).
+
+The data is stored as a CSV file in `_static/data/train.csv`, it contains 892
+samples with labels.
+
+#### Preprocessing
+
+In `task.py`, you'll find the preprocessing functions we'll apply to our data:
+
+- Passengers are grouped by age: 'Child' for 10 years and under,
+  'Adult' for ages between 11 and 40, and 'Elderly' for those over 40. If the age
+  isn't listed, we'll label it as 'Unknown'.
+
+  ```python3
+    def _bin_age(age_series):
+        bins = [-np.inf, 10, 40, np.inf]
+        labels = ["Child", "Adult", "Elderly"]
+        return (
+            pd.cut(age_series, bins=bins, labels=labels, right=True)
+            .astype(str)
+            .replace("nan", "Unknown")
+        )
+  ```
+
+- We pull out titles from passengers' names to help our model
+  understand social status and family roles, simplifying rare titles into a single
+  'Rare' category and converting any French titles to their English equivalents.
+
+  ```python3
+    def _extract_title(name_series):
+        titles = name_series.str.extract(" ([A-Za-z]+)\.", expand=False)
+        rare_titles = {
+            "Lady",
+            "Countess",
+            "Capt",
+            "Col",
+            "Don",
+            "Dr",
+            "Major",
+            "Rev",
+            "Sir",
+            "Jonkheer",
+            "Dona",
+        }
+        titles = titles.replace(list(rare_titles), "Rare")
+        titles = titles.replace({"Mlle": "Miss", "Ms": "Miss", "Mme": "Mrs"})
+        return titles
+  ```
+
+- The first letter of each cabin number is used to identify the
+  cabin area, with any missing entries marked as 'Unknown'. This could provide
+  insight into the passenger's location on the ship.
+
+- We remove features like 'PassengerId', 'Name', and
+  'Ticket' that won't be necessary for our model's predictions.
+
+- Lastly, we convert categorical data points such as 'Sex',
+  'Pclass', 'Embarked', 'Title', 'Cabin', and the binned 'Age' into One-Hot
+  encodings.
+
+  ```python3
+    def _create_features(df):
+        # Convert 'Age' to numeric, coercing errors to NaN
+        df["Age"] = pd.to_numeric(df["Age"], errors="coerce")
+        df["Age"] = _bin_age(df["Age"])
+        df["Cabin"] = df["Cabin"].str[0].fillna("Unknown")
+        df["Title"] = _extract_title(df["Name"])
+        df.drop(columns=["PassengerId", "Name", "Ticket"], inplace=True)
+        all_keywords = set(df.columns)
+        df = pd.get_dummies(
+            df, columns=["Sex", "Pclass", "Embarked", "Title", "Cabin", "Age"]
+        )
+        return df, all_keywords
+  ```
+
+#### Partitioning
+
+In `task.py`, we also partition our data for our 3 clients to mirror real-life
+collaborations where different organizations hold different feature sets:
+
+```python3
+def _partition_data(df, all_keywords):
+    partitions = []
+    keywords_sets = [{"Parch", "Cabin", "Pclass"}, {"Sex", "Title"}]
+    keywords_sets.append(all_keywords - keywords_sets[0] - keywords_sets[1])
+
+    for keywords in keywords_sets:
+        partitions.append(
+            df[
+                list(
+                    {
+                        col
+                        for col in df.columns
+                        for kw in keywords
+                        if kw in col or "Survived" in col
+                    }
+                )
+            ]
+        )
+
+    return partitions
+```
+
+Client 1: This client looks at family connections and accommodations, working
+with features like the number of parents and children each passenger had on
+board ('Parch'), the cabin number ('Cabin'), and the ticket class ('Pclass').
+
+Client 2: Here, the focus is on personal attributes. This client examines the
+passengers' gender ('Sex') and societal roles as indicated by their titles
+('Title').
+
+Client 3: The final client handles the rest of the data that the first two don't
+see. This includes the remaining features that give a broader view of the
+passengers' information.
+
+Each client is going to train their models on their own unique data without any
+idea of the passengers' survival outcomes, which we're trying to predict.
+
+Once all clients have done their part, we combine their insights to form a
+comprehensive understanding, just as if different organizations were pooling
+their knowledge while keeping their data private. This is the essence of
+Vertical Federated Learning: separate but together, each contributing to a
+collective intelligence without sharing sensitive information.
+
+Note that our final data processing function looks like that:
+
+```python3
+def get_partitions_and_label():
+    df = pd.read_csv("_static/data/train.csv")
+    processed_df = df.dropna(subset=["Embarked", "Fare"]).copy()
+    processed_df, all_keywords = _create_features(processed_df)
+    raw_partitions = _partition_data(processed_df, all_keywords)
+
+    partitions = []
+    for partition in raw_partitions:
+        partitions.append(partition.drop("Survived", axis=1))
+    return partitions, processed_df["Survived"].values
+```
+
+This returns the 3 partitions for our clients and the labels for our server.
+
+### Models
+
+#### Clients
+
+Each client's model is a neural network designed to operate on a distinct subset
+of features held by a client. In this example we will use simple linear
+regression models.
+
+```python3
+class ClientModel(nn.Module):
+    def __init__(self, input_size):
+        super(ClientModel, self).__init__()
+        self.fc = nn.Linear(input_size, 4)
+
+    def forward(self, x):
+        return self.fc(x)
+```
+
+The `input_size` corresponds to the number of features each client has, and this
+model maps those features to a 4-dimensional latent space. The outputs are
+essentially feature embeddings that capture the patterns within each client's
+data slice. These embeddings are then ready to be sent to the server for further
+processing.
+
+#### Server
+
+The server's model acts as the central aggregator in the VFL system. It's also a
+neural network but with a slightly different architecture tailored to its role
+in aggregating the client models' outputs.
+
+```python3
+class ServerModel(nn.Module):
+    def __init__(self):
+        super(ServerModel, self).__init__()
+        self.fc = nn.Linear(12, 1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.fc(x)
+        return self.sigmoid(x)
+```
+
+It comprises a single linear layer that accepts the concatenated outputs from
+all client models as its input. The number of inputs to this layer equals the
+total number of outputs from the client models ( $3 \times 4 = 12$ ). After processing
+these inputs, the linear layer's output is passed through a sigmoid activation
+function (`nn.Sigmoid()`), which maps the result to a `(0, 1)` range, providing
+a probability score indicative of the likelihood of survival.
+
+### Strategy
+
+The strategy we will write to perform the aggregation will inherit from `FedAvg`
+and set the following additional attributes:
+
+```python3
+self.model = ServerModel(12)
+self.initial_parameters = ndarrays_to_parameters(
+    [val.cpu().numpy() for _, val in self.model.state_dict().items()]
+)
+self.optimizer = optim.SGD(self.model.parameters(), lr=0.01)
+self.criterion = nn.BCELoss()
+self.label = torch.tensor(labels).float().unsqueeze(1)
+```
+
+With `labels` given as an argument to the strategy.
+
+We then redefine the `aggregate_fit` method:
+
+```python3
+def aggregate_fit(
+    self,
+    rnd,
+    results,
+    failures,
+):
+    # Do not aggregate if there are failures and failures are not accepted
+    if not self.accept_failures and failures:
+        return None, {}
+
+    # Convert results
+    embedding_results = [
+        torch.from_numpy(parameters_to_ndarrays(fit_res.parameters)[0])
+        for _, fit_res in results
+    ]
+    embeddings_aggregated = torch.cat(embedding_results, dim=1)
+    embedding_server = embeddings_aggregated.detach().requires_grad_()
+    output = self.model(embedding_server)
+    loss = self.criterion(output, self.label)
+    loss.backward()
+
+    self.optimizer.step()
+    self.optimizer.zero_grad()
+
+    grads = embedding_server.grad.split([4, 4, 4], dim=1)
+    np_grads = [grad.numpy() for grad in grads]
+    parameters_aggregated = ndarrays_to_parameters(np_grads)
+
+    with torch.no_grad():
+        correct = 0
+        output = self.model(embedding_server)
+        predicted = (output > 0.5).float()
+
+        correct += (predicted == self.label).sum().item()
+
+        accuracy = correct / len(self.label) * 100
+
+    metrics_aggregated = {"accuracy": accuracy}
+
+    return parameters_aggregated, metrics_aggregated
+```
+
+This is where all the magic happens. We first convert the `np.array`s that we
+received from our clients to `tensor`s, before concatenating the 3 embeddings
+together. This means that we go from 3 tensors of size `(892, 4)` to 1 tensor of
+size `(892, 12)`. The combined embeddings are fed through the server model to
+get the prediction output. The loss between the predicted output and the actual
+labels is calculated. Backward propagation is then performed to calculate the
+gradients, which are used to update the server model's parameters.
+
+The optimizer updates the server model's parameters based on the calculated
+gradients, and the gradients are reset to zero to prepare for the next round of
+aggregation.
+
+The gradients from the server model's embedding layer are then split according
+to the size of the output from each client model (assuming equal size for
+simplicity here), ready to be sent back to the respective client models.
+
+Finally, with no gradient calculation needed, the model's predictions are
+compared to the true labels to calculate the accuracy of the model after the
+update.
+
+Note that this `aggregate_fit` function returns gradients instead of trained
+weights. This is because, in this setting, sharing gradients allows each
+participant to benefit from the collective feedback gathered from the entire
+pool of data without the need to align their different feature spaces (trained
+weights are directly tied to specific features of the dataset but not gradients,
+which are just a measure of the sensitivity of the loss function to changes in
+the model's parameters). This shared feedback, encapsulated in the gradients,
+guides each participant's model to adjust and improve, achieving optimization
+not just based on its own data but also leveraging insights from the entire
+network's data.
+
+We do not need to return parameters here because updates are completed locally
+in VFL. But the server should still send the gradients back to all clients to
+let them continue the back prop and update their local model. In Flower, the
+parameters returned by `aggregate_fit` will be stored and sent to
+`Client.evaluate` via `configure_fit`. So we take advantage of this and return
+our gradients in `aggregate_fit` so that they'll be sent to `Client.evaluate` as
+`parameters`. That's also why we can obtain gradients from the `parameters`
+argument in `Client.evaluate` (see next section).
+
+The last thing we have to do is to redefine the `aggregate_evaluate` function to
+disable distributed evaluation (as the clients do not hold any labels to test
+their local models).
+
+```python3
+def aggregate_evaluate(
+    self,
+    rnd,
+    results,
+    failures,
+):
+    return None, {}
+```
+
+### Client class and function
+
+Our `FlowerClient` class is going to be quite straight forward.
+
+```python3
+class FlowerClient(fl.client.NumPyClient):
+    def __init__(self, cid, data):
+        self.cid = cid
+        self.train = torch.tensor(StandardScaler().fit_transform(data)).float()
+        self.model = ClientModel(input_size=self.train.shape[1])
+        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.01)
+        self.embedding = self.model(self.train)
+
+    def get_parameters(self, config):
+        pass
+
+    def fit(self, parameters, config):
+        self.embedding = self.model(self.train)
+        return [self.embedding.detach().numpy()], 1, {}
+
+    def evaluate(self, parameters, config):
+        self.model.zero_grad()
+        self.embedding.backward(torch.from_numpy(parameters[int(self.cid)]))
+        self.optimizer.step()
+        return None
+```
+
+After defining our model and data attributes (respectively `self.model` and
+`self.train`), we define our `fit` function as such: the `self.model(self.train)`
+performs a forward pass using the client's local training data (`self.train`).
+This generates the embeddings (feature representations) for the data. To conform
+with the return type of the `fit` function, we need to return a list of
+`np.array`s (hence the conversion), the number of samples, which won't be used
+on the server side, so we just return 1, and then an empty dict.
+
+For the `evaluate` function, we perform our model's backward pass using the
+gradients sent by the server and then update our local model's parameters based
+on those new gradients. Note that the `loss` and `num_examples` we return in our
+evaluate function are bogus, as they won't be used on the server side.
+
+The `client_fn` we will use in our `start_simulation` function to generate our 3
+clients will be very basic:
+
+```pyhton3
+partitions, label = get_partitions_and_label()
+
+def client_fn(cid):
+    return FlowerClient(cid, partitions[int(cid)]).to_client()
+```
+
+We pass a `client_id` and its corresponding partition to each client.
+
+### Evaluation
+
+Please note that we do not perform distributed evaluation. This is because only
+the server holds some labels to compare the results to. This is why the only
+evaluation we perform is on the server side.
+
+In this example, we use the `FlowerClient` `evaluate` function for
+backpropagation instead of using it for evaluation. We do this because we know
+that the `evaluate` function of the clients will be called after the fit
+function. This allows us to aggregate our models in `aggregate_fit` and then
+send them back to the clients using this `evaluate` function and perform the
+backpropagation. This is not done for evaluation, hence why we return `None` in
+the `aggregate_evaluate` function of the strategy.
+
+### Starting the simulation
+
+Putting everything together, to start our simulation we use the following
+function:
+
+```python3
+hist = fl.simulation.start_simulation(
+    client_fn=client_fn,
+    num_clients=3,
+    config=fl.server.ServerConfig(num_rounds=1000),
+    strategy=Strategy(label),
+)
+```
+
+As mentioned before, we train for 1000 rounds but it should still last only
+a few seconds.
+
+Note that we store the results of the simulation into `hist`, this will allow us
+to use the `plot.py` file to plot the accuracy as a function of the number of
+rounds.
+
+## Results
+
+Here we can observe the results after 1000 rounds:
+
+![Accuracy plot](_static/vfl-accuracy.png)
diff --git a/examples/vertical-fl/_static/data/train.csv b/examples/vertical-fl/_static/data/train.csv
new file mode 100644
index 000000000000..63b68ab0ba98
--- /dev/null
+++ b/examples/vertical-fl/_static/data/train.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
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pW&;?X2qbK?e&>bat5nUuL&*Ng@ehH22>e6f9|Hd`2t5Cb`9DfUDklH{

literal 0
HcmV?d00001

diff --git a/examples/vertical-fl/client.py b/examples/vertical-fl/client.py
new file mode 100644
index 000000000000..9f489e70f086
--- /dev/null
+++ b/examples/vertical-fl/client.py
@@ -0,0 +1,27 @@
+import flwr as fl
+import torch
+from sklearn.preprocessing import StandardScaler
+
+from task import ClientModel
+
+
+class FlowerClient(fl.client.NumPyClient):
+    def __init__(self, cid, data):
+        self.cid = cid
+        self.train = torch.tensor(StandardScaler().fit_transform(data)).float()
+        self.model = ClientModel(input_size=self.train.shape[1])
+        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.01)
+        self.embedding = self.model(self.train)
+
+    def get_parameters(self, config):
+        pass
+
+    def fit(self, parameters, config):
+        self.embedding = self.model(self.train)
+        return [self.embedding.detach().numpy()], 1, {}
+
+    def evaluate(self, parameters, config):
+        self.model.zero_grad()
+        self.embedding.backward(torch.from_numpy(parameters[int(self.cid)]))
+        self.optimizer.step()
+        return 0.0, 1, {}
diff --git a/examples/vertical-fl/plot.py b/examples/vertical-fl/plot.py
new file mode 100644
index 000000000000..801f202328c7
--- /dev/null
+++ b/examples/vertical-fl/plot.py
@@ -0,0 +1,8 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+if __name__ == "__main__":
+    hist = np.load("_static/results/hist.npy", allow_pickle=True).item()
+    rounds, values = zip(*hist.metrics_distributed_fit["accuracy"])
+    plt.plot(np.asarray(rounds), np.asarray(values))
+    plt.savefig("_static/results/accuracy.png")
diff --git a/examples/vertical-fl/pyproject.toml b/examples/vertical-fl/pyproject.toml
new file mode 100644
index 000000000000..3c3307f24f08
--- /dev/null
+++ b/examples/vertical-fl/pyproject.toml
@@ -0,0 +1,18 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "quickstart-pytorch"
+version = "0.1.0"
+description = "PyTorch Federated Learning Quickstart with Flower"
+authors = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+torch = "2.1.0"
+matplotlib = "3.7.3"
+scikit-learn = "1.3.2"
+numpy = "1.24.4"
+pandas = "2.0.3"
diff --git a/examples/vertical-fl/requirements.txt b/examples/vertical-fl/requirements.txt
new file mode 100644
index 000000000000..f5c4f6f7b453
--- /dev/null
+++ b/examples/vertical-fl/requirements.txt
@@ -0,0 +1,6 @@
+flwr>=1.0, <2.0
+torch==2.1.0
+matplotlib==3.7.3
+scikit-learn==1.3.2
+numpy==1.24.4
+pandas==2.0.3
diff --git a/examples/vertical-fl/simulation.py b/examples/vertical-fl/simulation.py
new file mode 100644
index 000000000000..a6b1184dd76c
--- /dev/null
+++ b/examples/vertical-fl/simulation.py
@@ -0,0 +1,22 @@
+import flwr as fl
+import numpy as np
+from strategy import Strategy
+from client import FlowerClient
+from task import get_partitions_and_label
+
+partitions, label = get_partitions_and_label()
+
+
+def client_fn(cid):
+    return FlowerClient(cid, partitions[int(cid)]).to_client()
+
+
+# Start Flower server
+hist = fl.simulation.start_simulation(
+    client_fn=client_fn,
+    num_clients=3,
+    config=fl.server.ServerConfig(num_rounds=1000),
+    strategy=Strategy(label),
+)
+
+np.save("_static/results/hist.npy", hist)
diff --git a/examples/vertical-fl/strategy.py b/examples/vertical-fl/strategy.py
new file mode 100644
index 000000000000..0744fa83662a
--- /dev/null
+++ b/examples/vertical-fl/strategy.py
@@ -0,0 +1,106 @@
+import flwr as fl
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from flwr.common import ndarrays_to_parameters, parameters_to_ndarrays
+
+
+class ServerModel(nn.Module):
+    def __init__(self, input_size):
+        super(ServerModel, self).__init__()
+        self.fc = nn.Linear(input_size, 1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.fc(x)
+        return self.sigmoid(x)
+
+
+class Strategy(fl.server.strategy.FedAvg):
+    def __init__(
+        self,
+        labels,
+        *,
+        fraction_fit=1,
+        fraction_evaluate=1,
+        min_fit_clients=2,
+        min_evaluate_clients=2,
+        min_available_clients=2,
+        evaluate_fn=None,
+        on_fit_config_fn=None,
+        on_evaluate_config_fn=None,
+        accept_failures=True,
+        initial_parameters=None,
+        fit_metrics_aggregation_fn=None,
+        evaluate_metrics_aggregation_fn=None,
+    ) -> None:
+        super().__init__(
+            fraction_fit=fraction_fit,
+            fraction_evaluate=fraction_evaluate,
+            min_fit_clients=min_fit_clients,
+            min_evaluate_clients=min_evaluate_clients,
+            min_available_clients=min_available_clients,
+            evaluate_fn=evaluate_fn,
+            on_fit_config_fn=on_fit_config_fn,
+            on_evaluate_config_fn=on_evaluate_config_fn,
+            accept_failures=accept_failures,
+            initial_parameters=initial_parameters,
+            fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,
+            evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn,
+        )
+        self.model = ServerModel(12)
+        self.initial_parameters = ndarrays_to_parameters(
+            [val.cpu().numpy() for _, val in self.model.state_dict().items()]
+        )
+        self.optimizer = optim.SGD(self.model.parameters(), lr=0.01)
+        self.criterion = nn.BCELoss()
+        self.label = torch.tensor(labels).float().unsqueeze(1)
+
+    def aggregate_fit(
+        self,
+        rnd,
+        results,
+        failures,
+    ):
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Convert results
+        embedding_results = [
+            torch.from_numpy(parameters_to_ndarrays(fit_res.parameters)[0])
+            for _, fit_res in results
+        ]
+        embeddings_aggregated = torch.cat(embedding_results, dim=1)
+        embedding_server = embeddings_aggregated.detach().requires_grad_()
+        output = self.model(embedding_server)
+        loss = self.criterion(output, self.label)
+        loss.backward()
+
+        self.optimizer.step()
+        self.optimizer.zero_grad()
+
+        grads = embedding_server.grad.split([4, 4, 4], dim=1)
+        np_grads = [grad.numpy() for grad in grads]
+        parameters_aggregated = ndarrays_to_parameters(np_grads)
+
+        with torch.no_grad():
+            correct = 0
+            output = self.model(embedding_server)
+            predicted = (output > 0.5).float()
+
+            correct += (predicted == self.label).sum().item()
+
+            accuracy = correct / len(self.label) * 100
+
+        metrics_aggregated = {"accuracy": accuracy}
+
+        return parameters_aggregated, metrics_aggregated
+
+    def aggregate_evaluate(
+        self,
+        rnd,
+        results,
+        failures,
+    ):
+        return None, {}
diff --git a/examples/vertical-fl/task.py b/examples/vertical-fl/task.py
new file mode 100644
index 000000000000..a3cd415229c5
--- /dev/null
+++ b/examples/vertical-fl/task.py
@@ -0,0 +1,90 @@
+import torch.nn as nn
+import numpy as np
+import pandas as pd
+
+
+def _bin_age(age_series):
+    bins = [-np.inf, 10, 40, np.inf]
+    labels = ["Child", "Adult", "Elderly"]
+    return (
+        pd.cut(age_series, bins=bins, labels=labels, right=True)
+        .astype(str)
+        .replace("nan", "Unknown")
+    )
+
+
+def _extract_title(name_series):
+    titles = name_series.str.extract(" ([A-Za-z]+)\.", expand=False)
+    rare_titles = {
+        "Lady",
+        "Countess",
+        "Capt",
+        "Col",
+        "Don",
+        "Dr",
+        "Major",
+        "Rev",
+        "Sir",
+        "Jonkheer",
+        "Dona",
+    }
+    titles = titles.replace(list(rare_titles), "Rare")
+    titles = titles.replace({"Mlle": "Miss", "Ms": "Miss", "Mme": "Mrs"})
+    return titles
+
+
+def _create_features(df):
+    # Convert 'Age' to numeric, coercing errors to NaN
+    df["Age"] = pd.to_numeric(df["Age"], errors="coerce")
+    df["Age"] = _bin_age(df["Age"])
+    df["Cabin"] = df["Cabin"].str[0].fillna("Unknown")
+    df["Title"] = _extract_title(df["Name"])
+    df.drop(columns=["PassengerId", "Name", "Ticket"], inplace=True)
+    all_keywords = set(df.columns)
+    df = pd.get_dummies(
+        df, columns=["Sex", "Pclass", "Embarked", "Title", "Cabin", "Age"]
+    )
+    return df, all_keywords
+
+
+def get_partitions_and_label():
+    df = pd.read_csv("_static/data/train.csv")
+    processed_df = df.dropna(subset=["Embarked", "Fare"]).copy()
+    processed_df, all_keywords = _create_features(processed_df)
+    raw_partitions = _partition_data(processed_df, all_keywords)
+
+    partitions = []
+    for partition in raw_partitions:
+        partitions.append(partition.drop("Survived", axis=1))
+    return partitions, processed_df["Survived"].values
+
+
+def _partition_data(df, all_keywords):
+    partitions = []
+    keywords_sets = [{"Parch", "Cabin", "Pclass"}, {"Sex", "Title"}]
+    keywords_sets.append(all_keywords - keywords_sets[0] - keywords_sets[1])
+
+    for keywords in keywords_sets:
+        partitions.append(
+            df[
+                list(
+                    {
+                        col
+                        for col in df.columns
+                        for kw in keywords
+                        if kw in col or "Survived" in col
+                    }
+                )
+            ]
+        )
+
+    return partitions
+
+
+class ClientModel(nn.Module):
+    def __init__(self, input_size):
+        super(ClientModel, self).__init__()
+        self.fc = nn.Linear(input_size, 4)
+
+    def forward(self, x):
+        return self.fc(x)

From c54feb63f393a4559e82a14c9f159cdf87894b93 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Mon, 27 Nov 2023 15:26:43 +0000
Subject: [PATCH 48/64] Format file in quickstart-pytorch example (#2639)

---
 examples/quickstart-pytorch/client.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/quickstart-pytorch/client.py b/examples/quickstart-pytorch/client.py
index 8ce19a45403d..f8c908ac631d 100644
--- a/examples/quickstart-pytorch/client.py
+++ b/examples/quickstart-pytorch/client.py
@@ -100,7 +100,8 @@ def apply_transforms(batch):
     "--node-id",
     choices=[0, 1, 2],
     type=int,
-    help="Partition of the dataset divided into 3 iid partitions created artificially.")
+    help="Partition of the dataset divided into 3 iid partitions created artificially.",
+)
 node_id = parser.parse_args().node_id
 
 # Load model and data (simple CNN, CIFAR-10)

From 1d105e6471e34670605e71bdf2756e1215392924 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 28 Nov 2023 18:12:50 +0100
Subject: [PATCH 49/64] Update changelog for 1.6 (#2640)

---
 dev/add-shortlog.sh         |  6 +++---
 doc/source/ref-changelog.md | 40 ++++++++++++++++++++++++++-----------
 2 files changed, 31 insertions(+), 15 deletions(-)

diff --git a/dev/add-shortlog.sh b/dev/add-shortlog.sh
index 2cd10f856aec..51f2e29b111c 100755
--- a/dev/add-shortlog.sh
+++ b/dev/add-shortlog.sh
@@ -3,10 +3,10 @@ set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 
 tags=$(git tag --sort=-v:refname)
-new_version=$(echo "$tags" | sed -n '1p')
-old_version=$(echo "$tags" | sed -n '2p')
+new_version=$1
+old_version=$(echo "$tags" | sed -n '1p')
 
-shortlog=$(git shortlog "$old_version".."$new_version" -s | grep -vEi '(\(|\[)bot(\)|\])' | awk '{name = substr($0, index($0, $2)); printf "%s`%s`", sep, name; sep=", "} END {print ""}')
+shortlog=$(git shortlog "$old_version"..main -s | grep -vEi '(\(|\[)bot(\)|\])' | awk '{name = substr($0, index($0, $2)); printf "%s`%s`", sep, name; sep=", "} END {print ""}')
 
 token="<!---TOKEN_$new_version-->"
 thanks="\n### Thanks to our contributors\n\nWe would like to give our special thanks to all the contributors who made the new version of Flower possible (in \`git shortlog\` order):\n\n$shortlog $token"
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 202d70b24ceb..e79daf39c778 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -2,10 +2,22 @@
 
 ## Unreleased
 
+## v1.6.0 (2023-11-28)
+
+### Thanks to our contributors
+
+We would like to give our special thanks to all the contributors who made the new version of Flower possible (in `git shortlog` order):
+
+`Aashish Kolluri`, `Adam Narozniak`, `Alessio Mora`, `Barathwaja S`, `Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Gabriel Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`, `Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, `Steve Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, `cnxdeveloper`, `k3nfalt` <!---TOKEN_v1.6.0-->
+
 ### What's new?
 
 - **Add experimental support for Python 3.12** ([#2565](https://github.com/adap/flower/pull/2565))
 
+- **Add new XGBoost examples** ([#2612](https://github.com/adap/flower/pull/2612), [#2554](https://github.com/adap/flower/pull/2554), [#2617](https://github.com/adap/flower/pull/2617), [#2618](https://github.com/adap/flower/pull/2618), [#2619](https://github.com/adap/flower/pull/2619), [#2567](https://github.com/adap/flower/pull/2567))
+
+  We have added a new `xgboost-quickstart` example alongside a new `xgboost-comprehensive` example that goes more in-depth.
+
 - **Add Vertical FL example** ([#2598](https://github.com/adap/flower/pull/2598))
 
   We had many questions about Vertical Federated Learning using Flower, so we decided to add an simple example for it on the [Titanic dataset](https://www.kaggle.com/competitions/titanic/data) alongside a tutorial (in the README).
@@ -14,17 +26,23 @@
 
 - **Update REST API to support create and delete nodes** ([#2283](https://github.com/adap/flower/pull/2283))
 
+- **Update the Android SDK** ([#2187](https://github/com/adap/flower/pull/2187))
+
+  Add gRPC request-response capability to the Android SDK.
+
 - **Update the C++ SDK** ([#2537](https://github/com/adap/flower/pull/2537), [#2528](https://github/com/adap/flower/pull/2528), [#2523](https://github.com/adap/flower/pull/2523), [#2522](https://github.com/adap/flower/pull/2522))
 
   Add gRPC request-response capability to the C++ SDK.
 
-- **Fix the incorrect return types of Strategy** ([#2432](https://github.com/adap/flower/pull/2432/files))
+- **Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), [#2390](https://github.com/adap/flower/pull/2390), [#2493](https://github.com/adap/flower/pull/2493))
 
-  The types of the return values in the docstrings in two methods (`aggregate_fit` and `aggregate_evaluate`) now match the hint types in the code.
+  Using the `client_fn`, Flower clients can interchangeably run as standalone processes (i.e. via `start_client`) or in simulation (i.e. via `start_simulation`) without requiring changes to how the client class is defined and instantiated. The `to_client()` function is introduced to convert a `NumPyClient` to a `Client`.
 
-- **Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), [#2390](https://github.com/adap/flower/pull/2390), [#2493](https://github.com/adap/flower/pull/2493))
+- **Add new** `Bulyan` **strategy** ([#1817](https://github.com/adap/flower/pull/1817), [#1891](https://github.com/adap/flower/pull/1891))
+
+  The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., 2018](https://arxiv.org/abs/1802.07927)
 
-  Using the `client_fn`, Flower clients can interchangeably run as standalone processes (i.e. via `start_client`) or in simulation (i.e. via `start_simulation`) without requiring changes to how the client class is defined and instantiated. Calling `start_numpy_client` is now deprecated.
+- **Add new** `XGB Bagging` **strategy** ([#2611](https://github.com/adap/flower/pull/2611))
 
 - **Update Flower Baselines**
 
@@ -52,19 +70,17 @@
 
   - FedBN ([#2608](https://github.com/adap/flower/pull/2608), [#2615](https://github.com/adap/flower/pull/2615))
 
-- **Update Flower Examples** ([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425), [#2526](https://github.com/adap/flower/pull/2526))
+- **General updates to Flower Examples** ([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425), [#2526](https://github.com/adap/flower/pull/2526), [#2302](https://github.com/adap/flower/pull/2302), [#2545](https://github.com/adap/flower/pull/2545))
 
-- **General updates to baselines** ([#2301](https://github.com/adap/flower/pull/2301), [#2305](https://github.com/adap/flower/pull/2305), [#2307](https://github.com/adap/flower/pull/2307), [#2327](https://github.com/adap/flower/pull/2327), [#2435](https://github.com/adap/flower/pull/2435))
+- **General updates to Flower Baselines** ([#2301](https://github.com/adap/flower/pull/2301), [#2305](https://github.com/adap/flower/pull/2305), [#2307](https://github.com/adap/flower/pull/2307), [#2327](https://github.com/adap/flower/pull/2327), [#2435](https://github.com/adap/flower/pull/2435), [#2462](https://github.com/adap/flower/pull/2462), [#2463](https://github.com/adap/flower/pull/2463), [#2461](https://github.com/adap/flower/pull/2461), [#2469](https://github.com/adap/flower/pull/2469), [#2466](https://github.com/adap/flower/pull/2466), [#2471](https://github.com/adap/flower/pull/2471), [#2472](https://github.com/adap/flower/pull/2472), [#2470](https://github.com/adap/flower/pull/2470))
 
-- **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331), [#2447](https://github.com/adap/flower/pull/2447), [#2448](https://github.com/adap/flower/pull/2448))
+- **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331), [#2447](https://github.com/adap/flower/pull/2447), [#2448](https://github.com/adap/flower/pull/2448), [#2294](https://github.com/adap/flower/pull/2294))
 
-- **General improvements** ([#2309](https://github.com/adap/flower/pull/2309), [#2310](https://github.com/adap/flower/pull/2310), [2313](https://github.com/adap/flower/pull/2313), [#2316](https://github.com/adap/flower/pull/2316), [2317](https://github.com/adap/flower/pull/2317),[#2349](https://github.com/adap/flower/pull/2349), [#2360](https://github.com/adap/flower/pull/2360), [#2402](https://github.com/adap/flower/pull/2402), [#2446](https://github.com/adap/flower/pull/2446) [#2561](https://github.com/adap/flower/pull/2561))
+- **General updates to Flower SDKs** ([#2288](https://github.com/adap/flower/pull/2288), [#2429](https://github.com/adap/flower/pull/2429), [#2555](https://github.com/adap/flower/pull/2555), [#2543](https://github.com/adap/flower/pull/2543), [#2544](https://github.com/adap/flower/pull/2544), [#2597](https://github.com/adap/flower/pull/2597), [#2623](https://github.com/adap/flower/pull/2623))
 
-  Flower received many improvements under the hood, too many to list here.
-
-- **Add new** `Bulyan` **strategy** ([#1817](https://github.com/adap/flower/pull/1817), [#1891](https://github.com/adap/flower/pull/1891))
+- **General improvements** ([#2309](https://github.com/adap/flower/pull/2309), [#2310](https://github.com/adap/flower/pull/2310), [2313](https://github.com/adap/flower/pull/2313), [#2316](https://github.com/adap/flower/pull/2316), [2317](https://github.com/adap/flower/pull/2317), [#2349](https://github.com/adap/flower/pull/2349), [#2360](https://github.com/adap/flower/pull/2360), [#2402](https://github.com/adap/flower/pull/2402), [#2446](https://github.com/adap/flower/pull/2446), [#2561](https://github.com/adap/flower/pull/2561), [#2273](https://github.com/adap/flower/pull/2273), [#2267](https://github.com/adap/flower/pull/2267), [#2274](https://github.com/adap/flower/pull/2274), [#2275](https://github.com/adap/flower/pull/2275), [#2432](https://github.com/adap/flower/pull/2432), [#2251](https://github.com/adap/flower/pull/2251), [#2321](https://github.com/adap/flower/pull/2321), [#1936](https://github.com/adap/flower/pull/1936), [#2408](https://github.com/adap/flower/pull/2408), [#2413](https://github.com/adap/flower/pull/2413), [#2401](https://github.com/adap/flower/pull/2401), [#2531](https://github.com/adap/flower/pull/2531), [#2534](https://github.com/adap/flower/pull/2534), [#2535](https://github.com/adap/flower/pull/2535), [#2521](https://github.com/adap/flower/pull/2521), [#2553](https://github.com/adap/flower/pull/2553), [#2596](https://github.com/adap/flower/pull/2596))
 
-  The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., 2018](https://arxiv.org/abs/1802.07927)
+  Flower received many improvements under the hood, too many to list here.
 
 ### Incompatible changes
 

From 65c8cc60c53893a923c79d3d840929ce2203f47d Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 28 Nov 2023 18:21:57 +0100
Subject: [PATCH 50/64] Remove unreleased section in changelog (#2647)

---
 doc/source/ref-changelog.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index e79daf39c778..9aa169dd97bd 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -1,7 +1,5 @@
 # Changelog
 
-## Unreleased
-
 ## v1.6.0 (2023-11-28)
 
 ### Thanks to our contributors

From 63462fad48b4525e91616f4c1b564d60e271b1f4 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 28 Nov 2023 18:27:46 +0100
Subject: [PATCH 51/64] Fix release doc (#2648)

---
 doc/source/contributor-how-to-release-flower.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/contributor-how-to-release-flower.rst b/doc/source/contributor-how-to-release-flower.rst
index cb33cf0b3c3f..2eef165c0ed0 100644
--- a/doc/source/contributor-how-to-release-flower.rst
+++ b/doc/source/contributor-how-to-release-flower.rst
@@ -10,7 +10,7 @@ Update the changelog (``changelog.md``) with all relevant changes that happened
 
 `GitHub: Compare v1.2.0...main <https://github.com/adap/flower/compare/v1.2.0...main>`_
 
-Thank the authors who contributed since the last release. This can be done by running the ``./dev/add-shortlog.sh`` convenience script (it can be ran multiple times and will update the names in the list if new contributors were added in the meantime).
+Thank the authors who contributed since the last release. This can be done by running the ``./dev/add-shortlog.sh <new version>`` convenience script (it can be ran multiple times and will update the names in the list if new contributors were added in the meantime).
 
 During the release
 ------------------

From 875b6c740cc6add24cb6bdb05c3017b429ad094e Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Tue, 28 Nov 2023 17:44:46 +0000
Subject: [PATCH 52/64] Make `flower-client` HTTPS by default (#2636)

Co-authored-by: Taner Topal <taner@flower.dev>
Co-authored-by: Daniel J. Beutel <daniel@flower.dev>
---
 e2e/bare-https/client.py                      |  6 +-
 e2e/bare/client.py                            |  5 +-
 e2e/fastai/client.py                          |  7 +-
 e2e/jax/client.py                             |  6 +-
 e2e/mxnet/client.py                           |  6 +-
 e2e/opacus/client.py                          |  6 +-
 e2e/pandas/client.py                          |  6 +-
 e2e/pytorch-lightning/client.py               |  6 +-
 e2e/pytorch/client.py                         |  6 +-
 e2e/scikit-learn/client.py                    |  6 +-
 e2e/strategies/client.py                      |  9 +++
 e2e/tabnet/client.py                          |  6 +-
 e2e/tensorflow/client.py                      |  6 +-
 e2e/test_driver.sh                            | 14 ++--
 src/py/flwr/client/app.py                     | 78 +++++++++++++++++--
 src/py/flwr/client/grpc_client/connection.py  |  2 +
 .../client/grpc_client/connection_test.py     |  2 +-
 .../client/grpc_rere_client/connection.py     |  6 +-
 src/py/flwr/client/rest_client/connection.py  |  1 +
 src/py/flwr/common/grpc.py                    | 17 +++-
 src/py/flwr/driver/grpc_driver.py             |  1 +
 21 files changed, 169 insertions(+), 33 deletions(-)

diff --git a/e2e/bare-https/client.py b/e2e/bare-https/client.py
index da04a320b1d2..20a5b4875ddf 100644
--- a/e2e/bare-https/client.py
+++ b/e2e/bare-https/client.py
@@ -23,8 +23,11 @@ def evaluate(self, parameters, config):
         return loss, 1, {"accuracy": accuracy}
 
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
 
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 if __name__ == "__main__":
     # Start Flower client
@@ -32,4 +35,5 @@ def client_fn(cid):
         server_address="127.0.0.1:8080", 
         client=FlowerClient(),
         root_certificates=Path("certificates/ca.crt").read_bytes(),
+        insecure=False,
     )
diff --git a/e2e/bare/client.py b/e2e/bare/client.py
index 5010d1810387..05b997ff4133 100644
--- a/e2e/bare/client.py
+++ b/e2e/bare/client.py
@@ -24,8 +24,11 @@ def evaluate(self, parameters, config):
         return loss, 1, {"accuracy": accuracy}
 
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
 
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 if __name__ == "__main__":
     # Start Flower client
diff --git a/e2e/fastai/client.py b/e2e/fastai/client.py
index 0f83cc330c45..4425fed25277 100644
--- a/e2e/fastai/client.py
+++ b/e2e/fastai/client.py
@@ -50,7 +50,12 @@ def evaluate(self, parameters, config):
 
 
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
+
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 
 if __name__ == "__main__":
diff --git a/e2e/jax/client.py b/e2e/jax/client.py
index 466a829575f6..495d6a671981 100644
--- a/e2e/jax/client.py
+++ b/e2e/jax/client.py
@@ -51,7 +51,11 @@ def evaluate(
         return float(loss), num_examples, {"loss": float(loss)}
 
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 if __name__ == "__main__":
     # Start Flower client
diff --git a/e2e/mxnet/client.py b/e2e/mxnet/client.py
index 1907d47f7c53..2f0b714e708c 100644
--- a/e2e/mxnet/client.py
+++ b/e2e/mxnet/client.py
@@ -130,7 +130,11 @@ def evaluate(self, parameters, config):
 
 
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 if __name__ == "__main__":
     # Start Flower client
diff --git a/e2e/opacus/client.py b/e2e/opacus/client.py
index 552060916154..2e5c363381fa 100644
--- a/e2e/opacus/client.py
+++ b/e2e/opacus/client.py
@@ -135,7 +135,11 @@ def evaluate(self, parameters, config):
 
 def client_fn(cid):
     model = Net()
-    return FlowerClient(model)
+    return FlowerClient(model).to_client()
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 if __name__ == "__main__":
     fl.client.start_numpy_client(
diff --git a/e2e/pandas/client.py b/e2e/pandas/client.py
index f7ff6fc2bccb..5b8670091cb3 100644
--- a/e2e/pandas/client.py
+++ b/e2e/pandas/client.py
@@ -34,7 +34,11 @@ def fit(
         )
 
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 if __name__ == "__main__":
     # Start Flower client
diff --git a/e2e/pytorch-lightning/client.py b/e2e/pytorch-lightning/client.py
index e05caf0b93f4..71b178eca8c3 100644
--- a/e2e/pytorch-lightning/client.py
+++ b/e2e/pytorch-lightning/client.py
@@ -53,7 +53,11 @@ def client_fn(cid):
     train_loader, val_loader, test_loader = mnist.load_data()
 
     # Flower client
-    return FlowerClient(model, train_loader, val_loader, test_loader)
+    return FlowerClient(model, train_loader, val_loader, test_loader).to_client()
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 def main() -> None:
     # Model and data
diff --git a/e2e/pytorch/client.py b/e2e/pytorch/client.py
index ae6c40c329ac..f4e7e0300a06 100644
--- a/e2e/pytorch/client.py
+++ b/e2e/pytorch/client.py
@@ -107,7 +107,11 @@ def set_parameters(model, parameters):
     return 
 
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 
 if __name__ == "__main__":
diff --git a/e2e/scikit-learn/client.py b/e2e/scikit-learn/client.py
index 1f2f0291c1ec..fdca96c1697a 100644
--- a/e2e/scikit-learn/client.py
+++ b/e2e/scikit-learn/client.py
@@ -44,7 +44,11 @@ def evaluate(self, parameters, config):  # type: ignore
         return loss, len(X_test), {"accuracy": accuracy}
         
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 if __name__ == "__main__":
     # Start Flower client
diff --git a/e2e/strategies/client.py b/e2e/strategies/client.py
index de321658c40f..eb4598cb5439 100644
--- a/e2e/strategies/client.py
+++ b/e2e/strategies/client.py
@@ -43,6 +43,15 @@ def evaluate(self, parameters, config):
         return loss, len(x_test), {"accuracy": accuracy}
 
 
+def client_fn(cid):
+    return FlowerClient().to_client()
+
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
+
+
 if __name__ == "__main__":
     # Start Flower client
     fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
diff --git a/e2e/tabnet/client.py b/e2e/tabnet/client.py
index 58982543b8bb..3c10df0c79f1 100644
--- a/e2e/tabnet/client.py
+++ b/e2e/tabnet/client.py
@@ -79,7 +79,11 @@ def evaluate(self, parameters, config):
 
 
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 if __name__ == "__main__":
     # Start Flower client
diff --git a/e2e/tensorflow/client.py b/e2e/tensorflow/client.py
index fe5b2a3351fc..4ad2d5ebda57 100644
--- a/e2e/tensorflow/client.py
+++ b/e2e/tensorflow/client.py
@@ -32,7 +32,11 @@ def evaluate(self, parameters, config):
         return loss, len(x_test), {"accuracy": accuracy}
 
 def client_fn(cid):
-    return FlowerClient()
+    return FlowerClient().to_client()
+
+flower = fl.flower.Flower(
+    client_fn=client_fn,
+)
 
 if __name__ == "__main__":
     # Start Flower client
diff --git a/e2e/test_driver.sh b/e2e/test_driver.sh
index e5baac20fa1f..ca54dbf4852f 100755
--- a/e2e/test_driver.sh
+++ b/e2e/test_driver.sh
@@ -4,20 +4,22 @@ set -e
 case "$1" in
   bare-https)
     ./generate.sh
-    cert_arg="--certificates certificates/ca.crt certificates/server.pem certificates/server.key"
+    server_arg="--certificates certificates/ca.crt certificates/server.pem certificates/server.key"
+    client_arg="--root-certificates certificates/ca.crt"
     ;;
   *)
-    cert_arg="--insecure"
+    server_arg="--insecure"
+    client_arg="--insecure"
     ;;
 esac
 
-timeout 2m flower-server $cert_arg --grpc-bidi --grpc-bidi-fleet-api-address 0.0.0.0:8080 &
+timeout 2m flower-server $server_arg &
 sleep 3
 
-python client.py &
+timeout 2m flower-client $client_arg --callable client:flower --server 127.0.0.1:9092 &
 sleep 3
 
-python client.py &
+timeout 2m flower-client $client_arg --callable client:flower --server 127.0.0.1:9092 &
 sleep 3
 
 timeout 2m python driver.py &
@@ -27,7 +29,7 @@ wait $pid
 res=$?
 
 if [[ "$res" = "0" ]];
-  then echo "Training worked correctly" && pkill python;
+  then echo "Training worked correctly" && pkill flower-client && pkill flower-server;
   else echo "Training had an issue" && exit 1;
 fi
 
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index b39dbbfc33c0..81bbee148c95 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -18,7 +18,8 @@
 import argparse
 import sys
 import time
-from logging import INFO
+from logging import INFO, WARN
+from pathlib import Path
 from typing import Callable, ContextManager, Optional, Tuple, Union
 
 from flwr.client.client import Client
@@ -50,6 +51,26 @@ def run_client() -> None:
 
     args = _parse_args_client().parse_args()
 
+    # Obtain certificates
+    if args.insecure:
+        if args.root_certificates is not None:
+            sys.exit(
+                "Conflicting options: The '--insecure' flag disables HTTPS, "
+                "but '--root-certificates' was also specified. Please remove "
+                "the '--root-certificates' option when running in insecure mode, "
+                "or omit '--insecure' to use HTTPS."
+            )
+        log(WARN, "Option `--insecure` was set. Starting insecure HTTP client.")
+        root_certificates = None
+    else:
+        # Load the certificates if provided, or load the system certificates
+        cert_path = args.root_certificates
+        if cert_path is None:
+            root_certificates = None
+        else:
+            root_certificates = Path(cert_path).read_bytes()
+
+    print(args.root_certificates)
     print(args.server)
     print(args.callable_dir)
     print(args.callable)
@@ -66,6 +87,8 @@ def _load() -> Flower:
         server_address=args.server,
         load_callable_fn=_load,
         transport="grpc-rere",  # Only
+        root_certificates=root_certificates,
+        insecure=args.insecure,
     )
 
 
@@ -75,6 +98,19 @@ def _parse_args_client() -> argparse.ArgumentParser:
         description="Start a long-running Flower client",
     )
 
+    parser.add_argument(
+        "--insecure",
+        action="store_true",
+        help="Run the client without HTTPS. By default, the client runs with "
+        "HTTPS enabled. Use this flag only if you understand the risks.",
+    )
+    parser.add_argument(
+        "--root-certificates",
+        metavar="ROOT_CERT",
+        type=str,
+        help="Specifies the path to the PEM-encoded root certificate file for "
+        "establishing secure HTTPS connections.",
+    )
     parser.add_argument(
         "--server",
         default="0.0.0.0:9092",
@@ -118,6 +154,7 @@ def start_client(
     client: Optional[Client] = None,
     grpc_max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,
     root_certificates: Optional[Union[bytes, str]] = None,
+    insecure: Optional[bool] = None,
     transport: Optional[str] = None,
 ) -> None:
     """Start a Flower client node which connects to a Flower server.
@@ -146,6 +183,9 @@ class `flwr.client.Client` (default: None)
         The PEM-encoded root certificates as a byte string or a path string.
         If provided, a secure connection using the certificates will be
         established to an SSL-enabled Flower server.
+    insecure : bool (default: True)
+        Starts an insecure gRPC connection when True. Enables HTTPS connection
+        when False, using system certificates if `root_certificates` is None.
     transport : Optional[str] (default: None)
         Configure the transport layer. Allowed values:
         - 'grpc-bidi': gRPC, bidirectional streaming
@@ -156,19 +196,25 @@ class `flwr.client.Client` (default: None)
     --------
     Starting a gRPC client with an insecure server connection:
 
+    >>> start_client(
+    >>>     server_address=localhost:8080,
+    >>>     client_fn=client_fn,
+    >>> )
+
+    Starting an SSL-enabled gRPC client using system certificates:
+
     >>> def client_fn(cid: str):
     >>>     return FlowerClient()
     >>>
     >>> start_client(
     >>>     server_address=localhost:8080,
     >>>     client_fn=client_fn,
+    >>>     insecure=False,
     >>> )
 
-    Starting an SSL-enabled gRPC client:
+    Starting an SSL-enabled gRPC client using provided certificates:
 
     >>> from pathlib import Path
-    >>> def client_fn(cid: str):
-    >>>     return FlowerClient()
     >>>
     >>> start_client(
     >>>     server_address=localhost:8080,
@@ -178,6 +224,9 @@ class `flwr.client.Client` (default: None)
     """
     event(EventType.START_CLIENT_ENTER)
 
+    if insecure is None:
+        insecure = root_certificates is None
+
     if load_callable_fn is None:
         _check_actionable_client(client, client_fn)
 
@@ -211,6 +260,7 @@ def _load_app() -> Flower:
         sleep_duration: int = 0
         with connection(
             address,
+            insecure,
             grpc_max_message_length,
             root_certificates,
         ) as conn:
@@ -270,6 +320,7 @@ def start_numpy_client(
     client: NumPyClient,
     grpc_max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,
     root_certificates: Optional[bytes] = None,
+    insecure: Optional[bool] = None,
     transport: Optional[str] = None,
 ) -> None:
     """Start a Flower NumPyClient which connects to a gRPC server.
@@ -293,6 +344,9 @@ def start_numpy_client(
         The PEM-encoded root certificates as a byte string or a path string.
         If provided, a secure connection using the certificates will be
         established to an SSL-enabled Flower server.
+    insecure : Optional[bool] (default: None)
+        Starts an insecure gRPC connection when True. Enables HTTPS connection
+        when False, using system certificates if `root_certificates` is None.
     transport : Optional[str] (default: None)
         Configure the transport layer. Allowed values:
         - 'grpc-bidi': gRPC, bidirectional streaming
@@ -301,16 +355,25 @@ def start_numpy_client(
 
     Examples
     --------
-    Starting a client with an insecure server connection:
+    Starting a gRPC client with an insecure server connection:
+
+    >>> start_numpy_client(
+    >>>     server_address=localhost:8080,
+    >>>     client=FlowerClient(),
+    >>> )
+
+    Starting an SSL-enabled gRPC client using system certificates:
 
     >>> start_numpy_client(
     >>>     server_address=localhost:8080,
     >>>     client=FlowerClient(),
+    >>>     insecure=False,
     >>> )
 
-    Starting an SSL-enabled gRPC client:
+    Starting an SSL-enabled gRPC client using provided certificates:
 
     >>> from pathlib import Path
+    >>>
     >>> start_numpy_client(
     >>>     server_address=localhost:8080,
     >>>     client=FlowerClient(),
@@ -340,6 +403,7 @@ def start_numpy_client(
         client=wrp_client,
         grpc_max_message_length=grpc_max_message_length,
         root_certificates=root_certificates,
+        insecure=insecure,
         transport=transport,
     )
 
@@ -348,7 +412,7 @@ def _init_connection(
     transport: Optional[str], server_address: str
 ) -> Tuple[
     Callable[
-        [str, int, Union[bytes, str, None]],
+        [str, bool, int, Union[bytes, str, None]],
         ContextManager[
             Tuple[
                 Callable[[], Optional[TaskIns]],
diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index cbef4ef99051..335d28e72828 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -45,6 +45,7 @@ def on_channel_state_change(channel_connectivity: str) -> None:
 @contextmanager
 def grpc_connection(
     server_address: str,
+    insecure: bool,
     max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,
     root_certificates: Optional[Union[bytes, str]] = None,
 ) -> Iterator[
@@ -100,6 +101,7 @@ def grpc_connection(
 
     channel = create_channel(
         server_address=server_address,
+        insecure=insecure,
         root_certificates=root_certificates,
         max_message_length=max_message_length,
     )
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index 0485fa41db35..e5944230e5af 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -93,7 +93,7 @@ def test_integration_connection() -> None:
     def run_client() -> int:
         messages_received: int = 0
 
-        with grpc_connection(server_address=f"[::]:{port}") as conn:
+        with grpc_connection(server_address=f"[::]:{port}", insecure=True) as conn:
             receive, send, _, _ = conn
 
             # Setup processing loop
diff --git a/src/py/flwr/client/grpc_rere_client/connection.py b/src/py/flwr/client/grpc_rere_client/connection.py
index 424e413dc484..30d407a52c53 100644
--- a/src/py/flwr/client/grpc_rere_client/connection.py
+++ b/src/py/flwr/client/grpc_rere_client/connection.py
@@ -51,10 +51,9 @@ def on_channel_state_change(channel_connectivity: str) -> None:
 @contextmanager
 def grpc_request_response(
     server_address: str,
+    insecure: bool,
     max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,  # pylint: disable=W0613
-    root_certificates: Optional[
-        Union[bytes, str]
-    ] = None,  # pylint: disable=unused-argument
+    root_certificates: Optional[Union[bytes, str]] = None,
 ) -> Iterator[
     Tuple[
         Callable[[], Optional[TaskIns]],
@@ -95,6 +94,7 @@ def grpc_request_response(
 
     channel = create_channel(
         server_address=server_address,
+        insecure=insecure,
         root_certificates=root_certificates,
         max_message_length=max_message_length,
     )
diff --git a/src/py/flwr/client/rest_client/connection.py b/src/py/flwr/client/rest_client/connection.py
index 092e543bf55b..d22b246dbd61 100644
--- a/src/py/flwr/client/rest_client/connection.py
+++ b/src/py/flwr/client/rest_client/connection.py
@@ -61,6 +61,7 @@
 # pylint: disable-next=too-many-statements
 def http_request_response(
     server_address: str,
+    insecure: bool,  # pylint: disable=unused-argument
     max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,  # pylint: disable=W0613
     root_certificates: Optional[
         Union[bytes, str]
diff --git a/src/py/flwr/common/grpc.py b/src/py/flwr/common/grpc.py
index 2857048f62a0..9d0543ea8c75 100644
--- a/src/py/flwr/common/grpc.py
+++ b/src/py/flwr/common/grpc.py
@@ -27,10 +27,19 @@
 
 def create_channel(
     server_address: str,
+    insecure: bool,
     root_certificates: Optional[bytes] = None,
     max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,
 ) -> grpc.Channel:
     """Create a gRPC channel, either secure or insecure."""
+    # Check for conflicting parameters
+    if insecure and root_certificates is not None:
+        raise ValueError(
+            "Invalid configuration: 'root_certificates' should not be provided "
+            "when 'insecure' is set to True. For an insecure connection, omit "
+            "'root_certificates', or set 'insecure' to False for a secure connection."
+        )
+
     # Possible options:
     # https://github.com/grpc/grpc/blob/v1.43.x/include/grpc/impl/codegen/grpc_types.h
     channel_options = [
@@ -38,14 +47,14 @@ def create_channel(
         ("grpc.max_receive_message_length", max_message_length),
     ]
 
-    if root_certificates is not None:
+    if insecure:
+        channel = grpc.insecure_channel(server_address, options=channel_options)
+        log(INFO, "Opened insecure gRPC connection (no certificates were passed)")
+    else:
         ssl_channel_credentials = grpc.ssl_channel_credentials(root_certificates)
         channel = grpc.secure_channel(
             server_address, ssl_channel_credentials, options=channel_options
         )
         log(INFO, "Opened secure gRPC connection using certificates")
-    else:
-        channel = grpc.insecure_channel(server_address, options=channel_options)
-        log(INFO, "Opened insecure gRPC connection (no certificates were passed)")
 
     return channel
diff --git a/src/py/flwr/driver/grpc_driver.py b/src/py/flwr/driver/grpc_driver.py
index a25de6f9f666..7dd0a0f501c5 100644
--- a/src/py/flwr/driver/grpc_driver.py
+++ b/src/py/flwr/driver/grpc_driver.py
@@ -66,6 +66,7 @@ def connect(self) -> None:
             return
         self.channel = create_channel(
             server_address=self.driver_service_address,
+            insecure=(self.certificates is None),
             root_certificates=self.certificates,
         )
         self.stub = DriverStub(self.channel)

From 1f33ba84da35b08b8b9ce53a4179b51ed0a377a4 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 28 Nov 2023 19:20:43 +0100
Subject: [PATCH 53/64] Add HTTPS changes to changelog (#2649)

Co-authored-by: Daniel J. Beutel <daniel@flower.dev>
---
 doc/source/ref-changelog.md | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 9aa169dd97bd..b1282d898586 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -24,14 +24,20 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Update REST API to support create and delete nodes** ([#2283](https://github.com/adap/flower/pull/2283))
 
-- **Update the Android SDK** ([#2187](https://github/com/adap/flower/pull/2187))
+- **Update the Android SDK** ([#2187](https://github.com/adap/flower/pull/2187))
 
   Add gRPC request-response capability to the Android SDK.
 
-- **Update the C++ SDK** ([#2537](https://github/com/adap/flower/pull/2537), [#2528](https://github/com/adap/flower/pull/2528), [#2523](https://github.com/adap/flower/pull/2523), [#2522](https://github.com/adap/flower/pull/2522))
+- **Update the C++ SDK** ([#2537](https://github.com/adap/flower/pull/2537), [#2528](https://github.com/adap/flower/pull/2528), [#2523](https://github.com/adap/flower/pull/2523), [#2522](https://github.com/adap/flower/pull/2522))
 
   Add gRPC request-response capability to the C++ SDK.
 
+- **Make HTTPS the new default** ([#2591](https://github.com/adap/flower/pull/2591), [#2636](https://github.com/adap/flower/pull/2636))
+
+  Flower is moving to HTTPS by default. The new `flower-server` requires passing `--certificates`, but users can enable `--insecure` to use HTTP for prototyping. The same applies to `flower-client`, which can either use user-provided credentials or gRPC-bundled certificates to connect to an HTTPS-enabled server or requires opt-out via passing `--insecure` to enable insecure HTTP connections.
+
+  For backward compatibility, `start_client()` and `start_numpy_client()` will still start in insecure mode by default. In a future release, insecure connections will require user opt-in by passing `insecure=True`.
+
 - **Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), [#2390](https://github.com/adap/flower/pull/2390), [#2493](https://github.com/adap/flower/pull/2493))
 
   Using the `client_fn`, Flower clients can interchangeably run as standalone processes (i.e. via `start_client`) or in simulation (i.e. via `start_simulation`) without requiring changes to how the client class is defined and instantiated. The `to_client()` function is introduced to convert a `NumPyClient` to a `Client`.

From bcf2dfc2dd890362ada6781d914c946eaa58df76 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 28 Nov 2023 19:35:05 +0100
Subject: [PATCH 54/64] Use version instead of commit hash for action (#2650)

---
 .github/workflows/framework-release.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/framework-release.yml b/.github/workflows/framework-release.yml
index 2d2b7d8a4c4f..1c55894a127b 100644
--- a/.github/workflows/framework-release.yml
+++ b/.github/workflows/framework-release.yml
@@ -53,7 +53,7 @@ jobs:
           cat body.md
           
       - name: Release
-        uses: softprops/action-gh-release@de2c0eb
+        uses: softprops/action-gh-release@v1
         with:
           body_path: ./body.md
           draft: true

From 231a81f950cfe8f0c45d16ad1b278fa779888909 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 28 Nov 2023 20:06:44 +0100
Subject: [PATCH 55/64] Update version to 1.7.0 (#2651)

---
 baselines/doc/source/conf.py                                  | 2 +-
 doc/source/conf.py                                            | 2 +-
 .../contributor-how-to-install-development-versions.rst       | 4 ++--
 examples/doc/source/conf.py                                   | 2 +-
 pyproject.toml                                                | 2 +-
 5 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/baselines/doc/source/conf.py b/baselines/doc/source/conf.py
index dd43080f299e..e1055aaed216 100644
--- a/baselines/doc/source/conf.py
+++ b/baselines/doc/source/conf.py
@@ -36,7 +36,7 @@
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
-release = "1.6.0"
+release = "1.7.0"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 87860e2c6e7b..8077d26aa6ae 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -85,7 +85,7 @@
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
-release = "1.6.0"
+release = "1.7.0"
 
 # -- General configuration ---------------------------------------------------
 
diff --git a/doc/source/contributor-how-to-install-development-versions.rst b/doc/source/contributor-how-to-install-development-versions.rst
index e8028a35d706..243f4ef97e8e 100644
--- a/doc/source/contributor-how-to-install-development-versions.rst
+++ b/doc/source/contributor-how-to-install-development-versions.rst
@@ -59,5 +59,5 @@ Open a development version of the same notebook from branch `branch-name` by cha
 Install a `whl` on Google Colab:
 
 1. In the vertical icon grid on the left hand side, select ``Files`` > ``Upload to session storage``
-2. Upload the whl (e.g., ``flwr-1.6.0-py3-none-any.whl``)
-3. Change ``!pip install -q 'flwr[simulation]' torch torchvision matplotlib`` to ``!pip install -q 'flwr-1.6.0-py3-none-any.whl[simulation]' torch torchvision matplotlib``
+2. Upload the whl (e.g., ``flwr-1.7.0-py3-none-any.whl``)
+3. Change ``!pip install -q 'flwr[simulation]' torch torchvision matplotlib`` to ``!pip install -q 'flwr-1.7.0-py3-none-any.whl[simulation]' torch torchvision matplotlib``
diff --git a/examples/doc/source/conf.py b/examples/doc/source/conf.py
index dcb1788ea336..01cbb48c1587 100644
--- a/examples/doc/source/conf.py
+++ b/examples/doc/source/conf.py
@@ -27,7 +27,7 @@
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
-release = "1.6.0"
+release = "1.7.0"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/pyproject.toml b/pyproject.toml
index ff71298c1c2d..2349d554a409 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "flwr"
-version = "1.6.0"
+version = "1.7.0"
 description = "Flower: A Friendly Federated Learning Framework"
 license = "Apache-2.0"
 authors = ["The Flower Authors <hello@flower.dev>"]

From b3c530a8ae694e2633e4bc243347a83c73ff3253 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 28 Nov 2023 20:18:34 +0100
Subject: [PATCH 56/64] Add unreleased to changelog (#2652)

---
 doc/source/ref-changelog.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index b1282d898586..0cf7aca63941 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -1,5 +1,7 @@
 # Changelog
 
+## Unreleased
+
 ## v1.6.0 (2023-11-28)
 
 ### Thanks to our contributors

From 02bb3ba734b85c5fcb7bca6d9a7381562f3fd8ce Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Wed, 29 Nov 2023 11:54:09 +0000
Subject: [PATCH 57/64] Fix vertical-fl example (#2655)

Co-authored-by: Charles Beauville <charlesbeauville@gmail.com>
---
 examples/vertical-fl/pyproject.toml   | 6 +++---
 examples/vertical-fl/requirements.txt | 2 +-
 examples/vertical-fl/simulation.py    | 5 ++++-
 3 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/examples/vertical-fl/pyproject.toml b/examples/vertical-fl/pyproject.toml
index 3c3307f24f08..14771c70062f 100644
--- a/examples/vertical-fl/pyproject.toml
+++ b/examples/vertical-fl/pyproject.toml
@@ -3,14 +3,14 @@ requires = ["poetry-core>=1.4.0"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
-name = "quickstart-pytorch"
+name = "vertical-fl"
 version = "0.1.0"
-description = "PyTorch Federated Learning Quickstart with Flower"
+description = "PyTorch Vertical FL with Flower"
 authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr = ">=1.0,<2.0"
+flwr = { extras = ["simulation"], version = ">=1.0,<2.0" }
 torch = "2.1.0"
 matplotlib = "3.7.3"
 scikit-learn = "1.3.2"
diff --git a/examples/vertical-fl/requirements.txt b/examples/vertical-fl/requirements.txt
index f5c4f6f7b453..aee341e4c554 100644
--- a/examples/vertical-fl/requirements.txt
+++ b/examples/vertical-fl/requirements.txt
@@ -1,4 +1,4 @@
-flwr>=1.0, <2.0
+flwr[simulation]>=1.0, <2.0
 torch==2.1.0
 matplotlib==3.7.3
 scikit-learn==1.3.2
diff --git a/examples/vertical-fl/simulation.py b/examples/vertical-fl/simulation.py
index a6b1184dd76c..095ec6e0c7ea 100644
--- a/examples/vertical-fl/simulation.py
+++ b/examples/vertical-fl/simulation.py
@@ -2,6 +2,7 @@
 import numpy as np
 from strategy import Strategy
 from client import FlowerClient
+from pathlib import Path
 from task import get_partitions_and_label
 
 partitions, label = get_partitions_and_label()
@@ -19,4 +20,6 @@ def client_fn(cid):
     strategy=Strategy(label),
 )
 
-np.save("_static/results/hist.npy", hist)
+results_dir = Path("_static/results")
+results_dir.mkdir(exist_ok=True)
+np.save(str(results_dir/"hist.npy"), hist)

From ded72a237d18aedc411abd37a8925d1add531d8f Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Wed, 29 Nov 2023 12:06:53 +0000
Subject: [PATCH 58/64] Inject `WorkloadState` into instantiated client (#2632)

Co-authored-by: Daniel J. Beutel <daniel@flower.dev>
---
 doc/source/ref-changelog.md                   |  2 ++
 src/py/flwr/client/client.py                  | 11 +++++++
 src/py/flwr/client/flower.py                  |  5 +--
 .../client/message_handler/message_handler.py | 32 +++++++++++++------
 .../message_handler/message_handler_test.py   | 13 ++++++--
 src/py/flwr/client/numpy_client.py            | 23 +++++++++++++
 6 files changed, 72 insertions(+), 14 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 0cf7aca63941..f73d3babda18 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -50,6 +50,8 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Add new** `XGB Bagging` **strategy** ([#2611](https://github.com/adap/flower/pull/2611))
 
+- **Introduce `WorkloadState`** ([#2564](https://github.com/adap/flower/pull/2564), [#2632](https://github.com/adap/flower/pull/2632))
+
 - **Update Flower Baselines**
 
   - FedProx ([#2210](https://github.com/adap/flower/pull/2210), [#2286](https://github.com/adap/flower/pull/2286), [#2509](https://github.com/adap/flower/pull/2509))
diff --git a/src/py/flwr/client/client.py b/src/py/flwr/client/client.py
index 03769c6b8bcf..280e0a8ca989 100644
--- a/src/py/flwr/client/client.py
+++ b/src/py/flwr/client/client.py
@@ -19,6 +19,7 @@
 
 from abc import ABC
 
+from flwr.client.workload_state import WorkloadState
 from flwr.common import (
     Code,
     EvaluateIns,
@@ -37,6 +38,8 @@
 class Client(ABC):
     """Abstract base class for Flower clients."""
 
+    state: WorkloadState
+
     def get_properties(self, ins: GetPropertiesIns) -> GetPropertiesRes:
         """Return set of client's properties.
 
@@ -138,6 +141,14 @@ def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
             metrics={},
         )
 
+    def get_state(self) -> WorkloadState:
+        """Get the workload state from this client."""
+        return self.state
+
+    def set_state(self, state: WorkloadState) -> None:
+        """Apply a workload state to this client."""
+        self.state = state
+
     def to_client(self) -> Client:
         """Return client (itself)."""
         return self
diff --git a/src/py/flwr/client/flower.py b/src/py/flwr/client/flower.py
index 9eeb41887e24..f0d4ce122524 100644
--- a/src/py/flwr/client/flower.py
+++ b/src/py/flwr/client/flower.py
@@ -79,13 +79,14 @@ def __init__(
     def __call__(self, fwd: Fwd) -> Bwd:
         """."""
         # Execute the task
-        task_res = handle(
+        task_res, state_updated = handle(
             client_fn=self.client_fn,
+            state=fwd.state,
             task_ins=fwd.task_ins,
         )
         return Bwd(
             task_res=task_res,
-            state=WorkloadState(state={}),
+            state=state_updated,
         )
 
 
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index 971f2fe56e31..0f3070cfb01a 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -30,6 +30,7 @@
 )
 from flwr.client.secure_aggregation import SecureAggregationHandler
 from flwr.client.typing import ClientFn
+from flwr.client.workload_state import WorkloadState
 from flwr.common import serde
 from flwr.proto.task_pb2 import SecureAggregation, Task, TaskIns, TaskRes
 from flwr.proto.transport_pb2 import ClientMessage, Reason, ServerMessage
@@ -77,13 +78,17 @@ def handle_control_message(task_ins: TaskIns) -> Tuple[Optional[TaskRes], int]:
     return None, 0
 
 
-def handle(client_fn: ClientFn, task_ins: TaskIns) -> TaskRes:
+def handle(
+    client_fn: ClientFn, state: WorkloadState, task_ins: TaskIns
+) -> Tuple[TaskRes, WorkloadState]:
     """Handle incoming TaskIns from the server.
 
     Parameters
     ----------
     client_fn : ClientFn
         A callable that instantiates a Client.
+    state : WorkloadState
+        A dataclass storing the state for the workload being executed by the client.
     task_ins: TaskIns
         The task instruction coming from the server, to be processed by the client.
 
@@ -96,6 +101,7 @@ def handle(client_fn: ClientFn, task_ins: TaskIns) -> TaskRes:
     if server_msg is None:
         # Instantiate the client
         client = client_fn("-1")
+        client.set_state(state)
         # Secure Aggregation
         if task_ins.task.HasField("sa") and isinstance(
             client, SecureAggregationHandler
@@ -112,22 +118,24 @@ def handle(client_fn: ClientFn, task_ins: TaskIns) -> TaskRes:
                     sa=SecureAggregation(named_values=serde.named_values_to_proto(res)),
                 ),
             )
-            return task_res
+            return task_res, client.get_state()
         raise NotImplementedError()
-    client_msg = handle_legacy_message(client_fn, server_msg)
+    client_msg, updated_state = handle_legacy_message(client_fn, state, server_msg)
     task_res = wrap_client_message_in_task_res(client_msg)
-    return task_res
+    return task_res, updated_state
 
 
 def handle_legacy_message(
-    client_fn: ClientFn, server_msg: ServerMessage
-) -> ClientMessage:
+    client_fn: ClientFn, state: WorkloadState, server_msg: ServerMessage
+) -> Tuple[ClientMessage, WorkloadState]:
     """Handle incoming messages from the server.
 
     Parameters
     ----------
     client_fn : ClientFn
         A callable that instantiates a Client.
+    state : WorkloadState
+        A dataclass storing the state for the workload being executed by the client.
     server_msg: ServerMessage
         The message coming from the server, to be processed by the client.
 
@@ -144,15 +152,19 @@ def handle_legacy_message(
 
     # Instantiate the client
     client = client_fn("-1")
+    client.set_state(state)
     # Execute task
+    message = None
     if field == "get_properties_ins":
-        return _get_properties(client, server_msg.get_properties_ins)
+        message = _get_properties(client, server_msg.get_properties_ins)
     if field == "get_parameters_ins":
-        return _get_parameters(client, server_msg.get_parameters_ins)
+        message = _get_parameters(client, server_msg.get_parameters_ins)
     if field == "fit_ins":
-        return _fit(client, server_msg.fit_ins)
+        message = _fit(client, server_msg.fit_ins)
     if field == "evaluate_ins":
-        return _evaluate(client, server_msg.evaluate_ins)
+        message = _evaluate(client, server_msg.evaluate_ins)
+    if message:
+        return message, client.get_state()
     raise UnknownServerMessage()
 
 
diff --git a/src/py/flwr/client/message_handler/message_handler_test.py b/src/py/flwr/client/message_handler/message_handler_test.py
index 0183f161f873..d7f410d81fc0 100644
--- a/src/py/flwr/client/message_handler/message_handler_test.py
+++ b/src/py/flwr/client/message_handler/message_handler_test.py
@@ -19,6 +19,7 @@
 
 from flwr.client import Client
 from flwr.client.typing import ClientFn
+from flwr.client.workload_state import WorkloadState
 from flwr.common import (
     EvaluateIns,
     EvaluateRes,
@@ -133,7 +134,11 @@ def test_client_without_get_properties() -> None:
     disconnect_task_res, actual_sleep_duration = handle_control_message(
         task_ins=task_ins
     )
-    task_res = handle(client_fn=_get_client_fn(client), task_ins=task_ins)
+    task_res, _ = handle(
+        client_fn=_get_client_fn(client),
+        state=WorkloadState(state={}),
+        task_ins=task_ins,
+    )
 
     if not task_res.HasField("task"):
         raise ValueError("Task value not found")
@@ -197,7 +202,11 @@ def test_client_with_get_properties() -> None:
     disconnect_task_res, actual_sleep_duration = handle_control_message(
         task_ins=task_ins
     )
-    task_res = handle(client_fn=_get_client_fn(client), task_ins=task_ins)
+    task_res, _ = handle(
+        client_fn=_get_client_fn(client),
+        state=WorkloadState(state={}),
+        task_ins=task_ins,
+    )
 
     if not task_res.HasField("task"):
         raise ValueError("Task value not found")
diff --git a/src/py/flwr/client/numpy_client.py b/src/py/flwr/client/numpy_client.py
index 883be2f3d554..8b0893ea30aa 100644
--- a/src/py/flwr/client/numpy_client.py
+++ b/src/py/flwr/client/numpy_client.py
@@ -19,6 +19,7 @@
 from typing import Callable, Dict, Tuple
 
 from flwr.client.client import Client
+from flwr.client.workload_state import WorkloadState
 from flwr.common import (
     Config,
     NDArrays,
@@ -69,6 +70,8 @@
 class NumPyClient(ABC):
     """Abstract base class for Flower clients using NumPy."""
 
+    state: WorkloadState
+
     def get_properties(self, config: Config) -> Dict[str, Scalar]:
         """Return a client's set of properties.
 
@@ -171,6 +174,14 @@ def evaluate(
         _ = (self, parameters, config)
         return 0.0, 0, {}
 
+    def get_state(self) -> WorkloadState:
+        """Get the workload state from this client."""
+        return self.state
+
+    def set_state(self, state: WorkloadState) -> None:
+        """Apply a workload state to this client."""
+        self.state = state
+
     def to_client(self) -> Client:
         """Convert to object to Client type and return it."""
         return _wrap_numpy_client(client=self)
@@ -267,9 +278,21 @@ def _evaluate(self: Client, ins: EvaluateIns) -> EvaluateRes:
     )
 
 
+def _get_state(self: Client) -> WorkloadState:
+    """Return state of underlying NumPyClient."""
+    return self.numpy_client.get_state()  # type: ignore
+
+
+def _set_state(self: Client, state: WorkloadState) -> None:
+    """Apply state to underlying NumPyClient."""
+    self.numpy_client.set_state(state)  # type: ignore
+
+
 def _wrap_numpy_client(client: NumPyClient) -> Client:
     member_dict: Dict[str, Callable] = {  # type: ignore
         "__init__": _constructor,
+        "get_state": _get_state,
+        "set_state": _set_state,
     }
 
     # Add wrapper type methods (if overridden)

From 14c9b2a0924308795a606c29df3fbad9f8c846a1 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Wed, 29 Nov 2023 14:49:54 +0000
Subject: [PATCH 59/64] Fix quickstart-pytorch (#2659)

---
 examples/quickstart-pytorch/client.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/quickstart-pytorch/client.py b/examples/quickstart-pytorch/client.py
index f8c908ac631d..ad57645002f8 100644
--- a/examples/quickstart-pytorch/client.py
+++ b/examples/quickstart-pytorch/client.py
@@ -60,9 +60,9 @@ def test(net, testloader):
     correct, loss = 0, 0.0
     with torch.no_grad():
         for batch in tqdm(testloader, "Testing"):
-            images = batch["img"]
-            labels = batch["label"]
-            outputs = net(images.to(DEVICE))
+            images = batch["img"].to(DEVICE)
+            labels = batch["label"].to(DEVICE)
+            outputs = net(images)
             loss += criterion(outputs, labels).item()
             correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
     accuracy = correct / len(testloader.dataset)

From de632b2da7f9dd97cd52320668d95f6c10a91894 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 29 Nov 2023 15:58:28 +0100
Subject: [PATCH 60/64] Fix get-latest-changelog script (#2656)

---
 dev/get-latest-changelog.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dev/get-latest-changelog.sh b/dev/get-latest-changelog.sh
index 8c03b257e309..1d4a6b6bf58f 100755
--- a/dev/get-latest-changelog.sh
+++ b/dev/get-latest-changelog.sh
@@ -9,9 +9,9 @@ tags=$(git tag --sort=-v:refname)
 new_version=$(echo "$tags" | sed -n '1p')
 old_version=$(echo "$tags" | sed -n '2p')
 
-awk -v start="$new_version" -v end="$old_version" '
+awk '{sub(/<!--.*-->/, ""); print}' doc/source/ref-changelog.md | awk -v start="$new_version" -v end="$old_version" '
     $0 ~ start {flag=1; next}
     $0 ~ end {flag=0}
     flag && !printed && /^$/ {next}  # skip the first blank line
     flag && !printed {printed=1}
-    flag' doc/source/ref-changelog.md
+    flag'

From d8f4be4f3749fadb5e7098e251279b21509c4574 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 29 Nov 2023 16:29:14 +0100
Subject: [PATCH 61/64] Use specific Ubuntu version for workflows (#2661)

---
 .github/workflows/android-release.yml   | 2 +-
 .github/workflows/cpp.yml               | 2 +-
 .github/workflows/framework-release.yml | 2 +-
 .github/workflows/update-pr.yml         | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/android-release.yml b/.github/workflows/android-release.yml
index ba11e1ee85e7..b08d2bb66863 100644
--- a/.github/workflows/android-release.yml
+++ b/.github/workflows/android-release.yml
@@ -16,7 +16,7 @@ jobs:
             working-directory: src/kotlin
     name: Release build and publish
     if: github.repository == 'adap/flower'
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     steps:
       - name: Check out code
         uses: actions/checkout@v4
diff --git a/.github/workflows/cpp.yml b/.github/workflows/cpp.yml
index 7b8656497bf5..a1d918aa5b29 100644
--- a/.github/workflows/cpp.yml
+++ b/.github/workflows/cpp.yml
@@ -11,7 +11,7 @@ on:
 jobs:
   build_and_test:
     name: Build and test
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
 
     steps:
     - uses: actions/checkout@v4
diff --git a/.github/workflows/framework-release.yml b/.github/workflows/framework-release.yml
index 1c55894a127b..eab15a51d217 100644
--- a/.github/workflows/framework-release.yml
+++ b/.github/workflows/framework-release.yml
@@ -9,7 +9,7 @@ jobs:
   publish:
     if: ${{ github.repository == 'adap/flower' }}
     name: Publish draft
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     steps:
       - name: Checkout
         uses: actions/checkout@v4
diff --git a/.github/workflows/update-pr.yml b/.github/workflows/update-pr.yml
index 5ffcf0c13694..78ef5bc86772 100644
--- a/.github/workflows/update-pr.yml
+++ b/.github/workflows/update-pr.yml
@@ -6,7 +6,7 @@ on:
       - 'main'
 jobs:
   autoupdate:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     steps:
       - name: Automatically update mergeable PRs
         uses: adRise/update-pr-branch@v0.7.0

From a1b15f079ab3f9d65ca6c21dd27a0bb6960bf981 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 29 Nov 2023 16:36:24 +0100
Subject: [PATCH 62/64] Improve script to prepare changelog for release (#2657)

---
 dev/{add-shortlog.sh => prepare-release-changelog.sh} | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)
 rename dev/{add-shortlog.sh => prepare-release-changelog.sh} (76%)

diff --git a/dev/add-shortlog.sh b/dev/prepare-release-changelog.sh
similarity index 76%
rename from dev/add-shortlog.sh
rename to dev/prepare-release-changelog.sh
index 51f2e29b111c..3f2a2ae325e9 100755
--- a/dev/add-shortlog.sh
+++ b/dev/prepare-release-changelog.sh
@@ -2,6 +2,9 @@
 set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 
+# Get the current date in the format YYYY-MM-DD
+current_date=$(date +"%Y-%m-%d")
+
 tags=$(git tag --sort=-v:refname)
 new_version=$1
 old_version=$(echo "$tags" | sed -n '1p')
@@ -14,8 +17,8 @@ thanks="\n### Thanks to our contributors\n\nWe would like to give our special th
 # Check if the token exists in the markdown file
 if ! grep -q "$token" doc/source/ref-changelog.md; then
     # If the token does not exist in the markdown file, append the new content after the version
-    awk -v version="$new_version" -v text="$thanks" \
-        '{print} $0 ~ "## " version {print text}' doc/source/ref-changelog.md > temp.md && mv temp.md doc/source/ref-changelog.md
+    awk -v version="$new_version" -v date="$current_date" -v text="$thanks" \
+        '{ if ($0 ~ "## Unreleased") print "## " version " (" date ")\n" text; else print $0 }' doc/source/ref-changelog.md > temp.md && mv temp.md doc/source/ref-changelog.md
 else
     # If the token exists, replace the line containing the token with the new shortlog
     awk -v token="$token" -v newlog="$shortlog $token" '{ if ($0 ~ token) print newlog; else print $0 }' doc/source/ref-changelog.md > temp.md && mv temp.md doc/source/ref-changelog.md

From eaa7ff94b0c8ce6d923009343ff5bbee183c3d3f Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Wed, 29 Nov 2023 22:27:43 +0000
Subject: [PATCH 63/64] Add `WorkloadState` to virtual clients (#2643)

---
 doc/source/ref-changelog.md                   |  2 +
 .../simulation/ray_transport/ray_actor.py     | 38 +++++++++++++------
 .../ray_transport/ray_client_proxy.py         |  7 ++--
 .../ray_transport/ray_client_proxy_test.py    | 13 ++++---
 4 files changed, 39 insertions(+), 21 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index f73d3babda18..8fbfb4843756 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -52,6 +52,8 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Introduce `WorkloadState`** ([#2564](https://github.com/adap/flower/pull/2564), [#2632](https://github.com/adap/flower/pull/2632))
 
+- **Introduce `WorkloadState`** ([#2564](https://github.com/adap/flower/pull/2564), [#2632](https://github.com/adap/flower/pull/2632))
+
 - **Update Flower Baselines**
 
   - FedProx ([#2210](https://github.com/adap/flower/pull/2210), [#2286](https://github.com/adap/flower/pull/2286), [#2509](https://github.com/adap/flower/pull/2509))
diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py
index e6ddfd001ff6..57ea0ed7b187 100644
--- a/src/py/flwr/simulation/ray_transport/ray_actor.py
+++ b/src/py/flwr/simulation/ray_transport/ray_actor.py
@@ -27,6 +27,7 @@
 
 from flwr import common
 from flwr.client import Client, ClientFn
+from flwr.client.workload_state import WorkloadState
 from flwr.common.logger import log
 from flwr.simulation.ray_transport.utils import check_clientfn_returns_client
 
@@ -60,7 +61,8 @@ def run(
         client_fn: ClientFn,
         job_fn: JobFn,
         cid: str,
-    ) -> Tuple[str, ClientRes]:
+        state: WorkloadState,
+    ) -> Tuple[str, ClientRes, WorkloadState]:
         """Run a client workload."""
         # Execute tasks and return result
         # return also cid which is needed to ensure results
@@ -68,8 +70,13 @@ def run(
         try:
             # Instantiate client (check 'Client' type is returned)
             client = check_clientfn_returns_client(client_fn(cid))
+            # Inject state
+            client.set_state(state)
             # Run client job
             job_results = job_fn(client)
+            # Retrieve state (potentially updated)
+            updated_state = client.get_state()
+            print(f"Actor finishing ({cid}) !!!: {updated_state = }")
         except Exception as ex:
             client_trace = traceback.format_exc()
             message = (
@@ -83,7 +90,7 @@ def run(
             )
             raise ClientException(str(message)) from ex
 
-        return cid, job_results
+        return cid, job_results, updated_state
 
 
 @ray.remote
@@ -231,16 +238,18 @@ def add_actors_to_pool(self, num_actors: int) -> None:
             self._idle_actors.extend(new_actors)
             self.num_actors += num_actors
 
-    def submit(self, fn: Any, value: Tuple[ClientFn, JobFn, str]) -> None:
+    def submit(
+        self, fn: Any, value: Tuple[ClientFn, JobFn, str, WorkloadState]
+    ) -> None:
         """Take idle actor and assign it a client workload.
 
         Submit a job to an actor by first removing it from the list of idle actors, then
         check if this actor was flagged to be removed from the pool
         """
-        client_fn, job_fn, cid = value
+        client_fn, job_fn, cid, state = value
         actor = self._idle_actors.pop()
         if self._check_and_remove_actor_from_pool(actor):
-            future = fn(actor, client_fn, job_fn, cid)
+            future = fn(actor, client_fn, job_fn, cid, state)
             future_key = tuple(future) if isinstance(future, List) else future
             self._future_to_actor[future_key] = (self._next_task_index, actor, cid)
             self._next_task_index += 1
@@ -249,10 +258,10 @@ def submit(self, fn: Any, value: Tuple[ClientFn, JobFn, str]) -> None:
             self._cid_to_future[cid]["future"] = future_key
 
     def submit_client_job(
-        self, actor_fn: Any, job: Tuple[ClientFn, JobFn, str]
+        self, actor_fn: Any, job: Tuple[ClientFn, JobFn, str, WorkloadState]
     ) -> None:
         """Submit a job while tracking client ids."""
-        _, _, cid = job
+        _, _, cid, _ = job
 
         # We need to put this behind a lock since .submit() involves
         # removing and adding elements from a dictionary. Which creates
@@ -289,15 +298,17 @@ def _is_future_ready(self, cid: str) -> bool:
 
         return self._cid_to_future[cid]["ready"]  # type: ignore
 
-    def _fetch_future_result(self, cid: str) -> ClientRes:
-        """Fetch result for VirtualClient from Object Store.
+    def _fetch_future_result(self, cid: str) -> Tuple[ClientRes, WorkloadState]:
+        """Fetch result and updated state for a VirtualClient from Object Store.
 
         The job submitted by the ClientProxy interfacing with client with cid=cid is
         ready. Here we fetch it from the object store and return.
         """
         try:
             future: ObjectRef[Any] = self._cid_to_future[cid]["future"]  # type: ignore
-            res_cid, res = ray.get(future)  # type: (str, ClientRes)
+            res_cid, res, updated_state = ray.get(
+                future
+            )  # type: (str, ClientRes, WorkloadState)
         except ray.exceptions.RayActorError as ex:
             log(ERROR, ex)
             if hasattr(ex, "actor_id"):
@@ -314,7 +325,7 @@ def _fetch_future_result(self, cid: str) -> ClientRes:
         # Reset mapping
         self._reset_cid_to_future_dict(cid)
 
-        return res
+        return res, updated_state
 
     def _flag_actor_for_removal(self, actor_id_hex: str) -> None:
         """Flag actor that should be removed from pool."""
@@ -399,7 +410,9 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None:
                     # Manually terminate the actor
                     actor.terminate.remote()
 
-    def get_client_result(self, cid: str, timeout: Optional[float]) -> ClientRes:
+    def get_client_result(
+        self, cid: str, timeout: Optional[float]
+    ) -> Tuple[ClientRes, WorkloadState]:
         """Get result from VirtualClient with specific cid."""
         # Loop until all jobs submitted to the pool are completed. Break early
         # if the result for the ClientProxy calling this method is ready
@@ -411,4 +424,5 @@ def get_client_result(self, cid: str, timeout: Optional[float]) -> ClientRes:
                 break
 
         # Fetch result belonging to the VirtualClient calling this method
+        # Return both result from tasks and (potentially) updated workload state
         return self._fetch_future_result(cid)
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
index c4fc311b48f4..9596acaf1d91 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
@@ -29,6 +29,7 @@
     maybe_call_get_parameters,
     maybe_call_get_properties,
 )
+from flwr.client.workload_state import WorkloadState
 from flwr.common.logger import log
 from flwr.server.client_proxy import ClientProxy
 from flwr.simulation.ray_transport.ray_actor import (
@@ -132,10 +133,10 @@ def __init__(
     def _submit_job(self, job_fn: JobFn, timeout: Optional[float]) -> ClientRes:
         try:
             self.actor_pool.submit_client_job(
-                lambda a, c_fn, j_fn, cid: a.run.remote(c_fn, j_fn, cid),
-                (self.client_fn, job_fn, self.cid),
+                lambda a, c_fn, j_fn, cid, state: a.run.remote(c_fn, j_fn, cid, state),
+                (self.client_fn, job_fn, self.cid, WorkloadState(state={})),
             )
-            res = self.actor_pool.get_client_result(self.cid, timeout)
+            res, _ = self.actor_pool.get_client_result(self.cid, timeout)
 
         except Exception as ex:
             if self.actor_pool.num_actors == 0:
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index 35a082678058..44cb4ec70471 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -22,6 +22,7 @@
 import ray
 
 from flwr.client import Client, NumPyClient
+from flwr.client.workload_state import WorkloadState
 from flwr.common import Code, GetPropertiesRes, Status
 from flwr.simulation.ray_transport.ray_actor import (
     ClientRes,
@@ -120,14 +121,14 @@ def test_cid_consistency_all_submit_first() -> None:
     for prox in proxies:
         job = job_fn(prox.cid)
         prox.actor_pool.submit_client_job(
-            lambda a, c_fn, j_fn, cid: a.run.remote(c_fn, j_fn, cid),
-            (prox.client_fn, job, prox.cid),
+            lambda a, c_fn, j_fn, cid, state: a.run.remote(c_fn, j_fn, cid, state),
+            (prox.client_fn, job, prox.cid, WorkloadState(state={})),
         )
 
     # fetch results one at a time
     shuffle(proxies)
     for prox in proxies:
-        res = prox.actor_pool.get_client_result(prox.cid, timeout=None)
+        res, _ = prox.actor_pool.get_client_result(prox.cid, timeout=None)
         res = cast(GetPropertiesRes, res)
         assert int(prox.cid) * pi == res.properties["result"]
 
@@ -145,14 +146,14 @@ def test_cid_consistency_without_proxies() -> None:
     for cid in cids:
         job = job_fn(cid)
         pool.submit_client_job(
-            lambda a, c_fn, j_fn, cid_: a.run.remote(c_fn, j_fn, cid_),
-            (get_dummy_client, job, cid),
+            lambda a, c_fn, j_fn, cid_, state: a.run.remote(c_fn, j_fn, cid_, state),
+            (get_dummy_client, job, cid, WorkloadState(state={})),
         )
 
     # fetch results one at a time
     shuffle(cids)
     for cid in cids:
-        res = pool.get_client_result(cid, timeout=None)
+        res, _ = pool.get_client_result(cid, timeout=None)
         res = cast(GetPropertiesRes, res)
         assert int(cid) * pi == res.properties["result"]
 

From 094e92519f1025a8bd1bdc5885a06dce83616349 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Thu, 30 Nov 2023 14:52:37 +0000
Subject: [PATCH 64/64] Move definitions of `Fwd` and `Bwd` to
 `client/typing.py` (#2669)

---
 examples/vertical-fl/simulation.py |  2 +-
 src/py/flwr/client/app.py          |  4 ++--
 src/py/flwr/client/flower.py       | 26 ++------------------------
 src/py/flwr/client/typing.py       | 22 ++++++++++++++++++++++
 src/py/flwr/flower/__init__.py     |  4 ++--
 5 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/examples/vertical-fl/simulation.py b/examples/vertical-fl/simulation.py
index 095ec6e0c7ea..f4befc3a073e 100644
--- a/examples/vertical-fl/simulation.py
+++ b/examples/vertical-fl/simulation.py
@@ -22,4 +22,4 @@ def client_fn(cid):
 
 results_dir = Path("_static/results")
 results_dir.mkdir(exist_ok=True)
-np.save(str(results_dir/"hist.npy"), hist)
+np.save(str(results_dir / "hist.npy"), hist)
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 81bbee148c95..7ce7d51d3d4b 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -23,8 +23,8 @@
 from typing import Callable, ContextManager, Optional, Tuple, Union
 
 from flwr.client.client import Client
-from flwr.client.flower import Bwd, Flower, Fwd
-from flwr.client.typing import ClientFn
+from flwr.client.flower import Flower
+from flwr.client.typing import Bwd, ClientFn, Fwd
 from flwr.common import GRPC_MAX_MESSAGE_LENGTH, EventType, event
 from flwr.common.address import parse_address
 from flwr.common.constant import (
diff --git a/src/py/flwr/client/flower.py b/src/py/flwr/client/flower.py
index f0d4ce122524..5b083ee11b9f 100644
--- a/src/py/flwr/client/flower.py
+++ b/src/py/flwr/client/flower.py
@@ -16,32 +16,10 @@
 
 
 import importlib
-from dataclasses import dataclass
-from typing import Callable, cast
+from typing import cast
 
 from flwr.client.message_handler.message_handler import handle
-from flwr.client.typing import ClientFn
-from flwr.client.workload_state import WorkloadState
-from flwr.proto.task_pb2 import TaskIns, TaskRes
-
-
-@dataclass
-class Fwd:
-    """."""
-
-    task_ins: TaskIns
-    state: WorkloadState
-
-
-@dataclass
-class Bwd:
-    """."""
-
-    task_res: TaskRes
-    state: WorkloadState
-
-
-FlowerCallable = Callable[[Fwd], Bwd]
+from flwr.client.typing import Bwd, ClientFn, Fwd
 
 
 class Flower:
diff --git a/src/py/flwr/client/typing.py b/src/py/flwr/client/typing.py
index 7ee6f069768c..2c1f7506592c 100644
--- a/src/py/flwr/client/typing.py
+++ b/src/py/flwr/client/typing.py
@@ -14,8 +14,30 @@
 # ==============================================================================
 """Custom types for Flower clients."""
 
+from dataclasses import dataclass
 from typing import Callable
 
+from flwr.client.workload_state import WorkloadState
+from flwr.proto.task_pb2 import TaskIns, TaskRes
+
 from .client import Client as Client
 
+
+@dataclass
+class Fwd:
+    """."""
+
+    task_ins: TaskIns
+    state: WorkloadState
+
+
+@dataclass
+class Bwd:
+    """."""
+
+    task_res: TaskRes
+    state: WorkloadState
+
+
+FlowerCallable = Callable[[Fwd], Bwd]
 ClientFn = Callable[[str], Client]
diff --git a/src/py/flwr/flower/__init__.py b/src/py/flwr/flower/__init__.py
index 090c78062d02..892a7ce5afdc 100644
--- a/src/py/flwr/flower/__init__.py
+++ b/src/py/flwr/flower/__init__.py
@@ -15,9 +15,9 @@
 """Flower callable package."""
 
 
-from flwr.client.flower import Bwd as Bwd
 from flwr.client.flower import Flower as Flower
-from flwr.client.flower import Fwd as Fwd
+from flwr.client.typing import Bwd as Bwd
+from flwr.client.typing import Fwd as Fwd
 
 __all__ = [
     "Flower",