From 36eb11c0cae940f6bbd834ddff664f8a71748a16 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 20 Sep 2023 12:35:07 +0200
Subject: [PATCH 01/60] Add FDS how-to guides (#2332)

---
 .../how-to-disable-enable-progress-bar.rst    | 16 ++++
 .../source/how-to-install-flwr-datasets.rst   | 46 ++++++++++++
 datasets/doc/source/how-to-use-with-numpy.rst | 61 +++++++++++++++
 .../doc/source/how-to-use-with-pytorch.rst    | 67 +++++++++++++++++
 .../doc/source/how-to-use-with-tensorflow.rst | 74 +++++++++++++++++++
 5 files changed, 264 insertions(+)
 create mode 100644 datasets/doc/source/how-to-disable-enable-progress-bar.rst
 create mode 100644 datasets/doc/source/how-to-install-flwr-datasets.rst
 create mode 100644 datasets/doc/source/how-to-use-with-numpy.rst
 create mode 100644 datasets/doc/source/how-to-use-with-pytorch.rst
 create mode 100644 datasets/doc/source/how-to-use-with-tensorflow.rst

diff --git a/datasets/doc/source/how-to-disable-enable-progress-bar.rst b/datasets/doc/source/how-to-disable-enable-progress-bar.rst
new file mode 100644
index 000000000000..95a9c7a562b1
--- /dev/null
+++ b/datasets/doc/source/how-to-disable-enable-progress-bar.rst
@@ -0,0 +1,16 @@
+Disable/Enable Progress Bar
+===========================
+
+You will see a progress bar by default when you download a dataset or apply a map function. Here is how you control
+this behavior.
+
+Disable::
+
+  from datasets.utils.logging import disable_progress_bar
+  disable_progress_bar()
+
+Enable::
+
+  from datasets.utils.logging import enable_progress_bar
+  enable_progress_bar()
+
diff --git a/datasets/doc/source/how-to-install-flwr-datasets.rst b/datasets/doc/source/how-to-install-flwr-datasets.rst
new file mode 100644
index 000000000000..d2fd7923a817
--- /dev/null
+++ b/datasets/doc/source/how-to-install-flwr-datasets.rst
@@ -0,0 +1,46 @@
+Installation
+============
+
+Python Version
+--------------
+
+Flower Datasets requires `Python 3.8 <https://docs.python.org/3.8/>`_ or above.
+
+
+Install stable release (pip)
+----------------------------
+
+Stable releases are available on `PyPI <https://pypi.org/project/flwr_datasets/>`_
+
+.. code-block:: bash
+
+  python -m pip install flwr-datasets
+
+For vision datasets (e.g. MNIST, CIFAR10) ``flwr-datasets`` should be installed with the ``vision`` extra
+
+.. code-block:: bash
+
+  python -m pip install flwr_datasets[vision]
+
+For audio datasets (e.g. Speech Command) ``flwr-datasets`` should be installed with the ``audio`` extra
+
+.. code-block:: bash
+
+  python -m pip install flwr_datasets[audio]
+
+
+Verify installation
+-------------------
+
+The following command can be used to verify if Flower Datasets was successfully installed:
+
+.. code-block:: bash
+
+  python -c "import flwr_datasets;print(flwr_datasets.__version__)"
+
+If everything worked, it should print the version of Flower Datasets to the command line:
+
+.. code-block:: none
+
+  0.0.1
+
diff --git a/datasets/doc/source/how-to-use-with-numpy.rst b/datasets/doc/source/how-to-use-with-numpy.rst
new file mode 100644
index 000000000000..c3fbf85969e3
--- /dev/null
+++ b/datasets/doc/source/how-to-use-with-numpy.rst
@@ -0,0 +1,61 @@
+Use with NumPy
+==============
+
+Let's integrate ``flwr-datasets`` with NumPy.
+
+Prepare the desired partitioning::
+
+  from flwr_datasets import FederatedDataset
+
+  fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+  partition = fds.load_partition(0, "train")
+  centralized_dataset = fds.load_full("test")
+
+Transform to NumPy::
+
+  partition_np = partition.with_format("numpy")
+  X_train, y_train = partition_np["img"], partition_np["label"]
+
+That's all. Let's check the dimensions and data types of our ``X_train`` and ``y_train``::
+
+  print(f"The shape of X_train is: {X_train.shape}, dtype: {X_train.dtype}.")
+  print(f"The shape of y_train is: {y_train.shape}, dtype: {y_train.dtype}.")
+
+You should see::
+
+  The shape of X_train is: (500, 32, 32, 3), dtype: uint8.
+  The shape of y_train is: (500,), dtype: int64.
+
+Note that the ``X_train`` values are of type ``uint8``. It is not a problem for the TensorFlow model when passing the
+data as input, but it might remind us to normalize the data - global normalization, pre-channel normalization, or simply
+rescale the data to [0, 1] range::
+
+  X_train = (X_train - X_train.mean()) / X_train.std() # Global normalization
+
+
+CNN Keras model
+---------------
+Here's a quick example of how you can use that data with a simple CNN model::
+
+  import tensorflow as tf
+  from tensorflow.keras import datasets, layers, models
+
+  model = models.Sequential([
+      layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
+      layers.MaxPooling2D(2, 2),
+      layers.Conv2D(64, (3, 3), activation='relu'),
+      layers.MaxPooling2D(2, 2),
+      layers.Conv2D(64, (3, 3), activation='relu'),
+      layers.Flatten(),
+      layers.Dense(64, activation='relu'),
+      layers.Dense(10, activation='softmax')
+  ])
+
+  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+  model.fit(X_train, y_train, epochs=20, batch_size=64)
+
+You should see about 98% accuracy on the training data at the end of the training.
+
+Note that we used ``"sparse_categorical_crossentropy"``. Make sure to keep it that way if you don't want to one-hot-encode
+the labels.
diff --git a/datasets/doc/source/how-to-use-with-pytorch.rst b/datasets/doc/source/how-to-use-with-pytorch.rst
new file mode 100644
index 000000000000..5981f88c26b8
--- /dev/null
+++ b/datasets/doc/source/how-to-use-with-pytorch.rst
@@ -0,0 +1,67 @@
+Use with PyTorch
+================
+Let's integrate ``flwr-datasets`` with PyTorch DataLoaders and keep your PyTorch Transform applied to the data.
+
+Standard setup - download the dataset, choose the partitioning::
+
+  from flwr_datasets import FederatedDataset
+
+  fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+  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
+vary e.g. "img" or "image", "label" or "labels"::
+
+  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)}
+
+Apply Transforms, Create DataLoader. We will use the `map() <https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/main_classes#datasets.Dataset.map>`_
+function. Please note that the map will modify the existing dataset if the key in the dictionary you return is already present
+and append a new feature if it did not exist before. Below, we modify the "img" feature of our dataset.::
+
+  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")
+  dataloader = DataLoader(partition_torch, batch_size=64)
+
+We advise you to keep the
+`ToTensor() <https://pytorch.org/vision/stable/generated/torchvision.transforms.ToTensor.html>`_ transform (especially if
+you used it in your PyTorch code) because it swaps the dimensions from (H x W x C) to (C x H x W). This order is
+expected by a model with a convolutional layer.
+
+If you want to divide the dataset, you can use (at any point before passing the dataset to the DataLoader)::
+
+  partition_train_test = partition.train_test_split(test_size=0.2)
+  partition_train = partition_train_test["train"]
+  partition_test = partition_train_test["test"]
+
+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):]
+
+And during the training loop, you need to apply one change. With a typical dataloader, you get a list returned for each iteration::
+
+  for batch in all_from_pytorch_dataloader:
+    images, labels = batch
+    # Or alternatively:
+    # images, labels = batch[0], batch[1]
+
+With this dataset, you get a dictionary, and you access the data a little bit differently (via keys not by index)::
+
+  for batch in dataloader:
+    images, labels = batch["img"], batch["label"]
+
diff --git a/datasets/doc/source/how-to-use-with-tensorflow.rst b/datasets/doc/source/how-to-use-with-tensorflow.rst
new file mode 100644
index 000000000000..86a1f4e0da8a
--- /dev/null
+++ b/datasets/doc/source/how-to-use-with-tensorflow.rst
@@ -0,0 +1,74 @@
+Use with TensorFlow
+===================
+
+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.
+
+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
+follow the :doc:`how-to-use-with-numpy` tutorial, especially if you are a beginner.
+
+.. _tensorflow-dataset:
+
+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::
+
+  tf_dataset = partition.to_tf_dataset(columns="img", label_cols="label", batch_size=64,
+                                     shuffle=True)
+  # Assuming you have defined your model and compiled it
+  model.fit(tf_dataset, epochs=20)
+
+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 ::
+
+  data_tf = partition.with_format("tf")
+  # Assuming you have defined your model and compiled it
+  model.fit(data_tf["img"], data_tf["label"], epochs=20, batch_size=64)
+
+CNN Keras Model
+---------------
+Here's a quick example of how you can use that data with a simple CNN model (it assumes you created the TensorFlow
+dataset as in the section above, see :ref:`TensorFlow Dataset <tensorflow-dataset>`)::
+
+  import tensorflow as tf
+  from tensorflow.keras import datasets, layers, models
+
+  model = models.Sequential([
+      layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
+      layers.MaxPooling2D(2, 2),
+      layers.Conv2D(64, (3, 3), activation='relu'),
+      layers.MaxPooling2D(2, 2),
+      layers.Conv2D(64, (3, 3), activation='relu'),
+      layers.Flatten(),
+      layers.Dense(64, activation='relu'),
+      layers.Dense(10, activation='softmax')
+  ])
+
+  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+  model.fit(tf_dataset, epochs=20)
+

From d434595e315fc204b9d48c2ff471cb9ceb5f5f64 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 21 Sep 2023 11:18:05 +0200
Subject: [PATCH 02/60] Add paths specification to CI triggers for FDS (#2399)

---------

Co-authored-by: Taner Topal <taner@flower.dev>
---
 .github/workflows/datasets.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/workflows/datasets.yml b/.github/workflows/datasets.yml
index 60eb9e49db45..47e9f2aed926 100644
--- a/.github/workflows/datasets.yml
+++ b/.github/workflows/datasets.yml
@@ -4,9 +4,13 @@ on:
   push:
     branches:
       - main
+    paths:
+      - "datasets/**"
   pull_request:
     branches:
       - main
+    paths:
+      - "datasets/**"
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.event.pull_request.number || github.ref }}

From a43044c3476a5d6178afc6a615ad36264239ba2a Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 21 Sep 2023 22:52:04 +0200
Subject: [PATCH 03/60] Add TensorFlow integration tests with FDS (#2350)

---
 datasets/e2e/tensorflow/pyproject.toml     |  15 +++
 datasets/e2e/tensorflow/tensorflow_test.py | 102 +++++++++++++++++++++
 2 files changed, 117 insertions(+)
 create mode 100644 datasets/e2e/tensorflow/pyproject.toml
 create mode 100644 datasets/e2e/tensorflow/tensorflow_test.py

diff --git a/datasets/e2e/tensorflow/pyproject.toml b/datasets/e2e/tensorflow/pyproject.toml
new file mode 100644
index 000000000000..9c5c72c46400
--- /dev/null
+++ b/datasets/e2e/tensorflow/pyproject.toml
@@ -0,0 +1,15 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "fds-e2-tensorflow"
+version = "0.1.0"
+description = "Flower Datasets with TensorFlow"
+authors = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+flwr-datasets = { path = "./../../", extras = ["vision"] }
+tensorflow-cpu = "^2.9.1, !=2.11.1"
+parameterized = "==0.9.0"
diff --git a/datasets/e2e/tensorflow/tensorflow_test.py b/datasets/e2e/tensorflow/tensorflow_test.py
new file mode 100644
index 000000000000..e041bcb8f8cc
--- /dev/null
+++ b/datasets/e2e/tensorflow/tensorflow_test.py
@@ -0,0 +1,102 @@
+import unittest
+
+import numpy as np
+import tensorflow as tf
+from datasets.utils.logging import disable_progress_bar
+from parameterized import parameterized_class, parameterized
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+from tensorflow.keras.models import Sequential
+
+from flwr_datasets import FederatedDataset
+
+
+def SimpleCNN():
+    model = Sequential([
+        Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
+        MaxPooling2D(2, 2),
+        Conv2D(64, (3, 3), activation='relu'),
+        MaxPooling2D(2, 2),
+        Flatten(),
+        Dense(64, activation='relu'),
+        Dense(10, activation='softmax')
+    ])
+    return model
+
+
+@parameterized_class(
+    [
+        {"dataset_name": "cifar10", "test_split": "test"},
+        {"dataset_name": "cifar10", "test_split": "test"},
+    ]
+)
+class FdsToTensorFlow(unittest.TestCase):
+    """Test the conversion from FDS to PyTorch Dataset and Dataloader."""
+
+    dataset_name = ""
+    test_split = ""
+    expected_img_shape_after_transform = [32, 32, 3]
+
+    @classmethod
+    def setUpClass(cls):
+        """Disable progress bar to keep the log clean.
+        """
+        disable_progress_bar()
+
+    def _create_tensorflow_dataset(self, batch_size: int) -> tf.data.Dataset:
+        """Create a tensorflow dataset from the FederatedDataset."""
+        partition_id = 0
+        fds = FederatedDataset(dataset=self.dataset_name, partitioners={"train": 100})
+        partition = fds.load_partition(partition_id, "train")
+        tf_dataset = partition.to_tf_dataset(columns="img", label_cols="label",
+                                             batch_size=batch_size,
+                                             shuffle=False)
+        return tf_dataset
+
+    def test_create_partition_dataset_shape(self) -> None:
+        """Test if the DataLoader returns batches with the expected shape."""
+        batch_size = 16
+        dataset = self._create_tensorflow_dataset(batch_size)
+        batch = next(iter(dataset))
+        images = batch[0]
+        self.assertEqual(tuple(images.shape),
+                         (batch_size, *self.expected_img_shape_after_transform))
+
+    def test_create_partition_dataloader_with_transforms_batch_type(self) -> None:
+        """Test if the DataLoader returns batches of type dictionary."""
+        batch_size = 16
+        dataset = self._create_tensorflow_dataset(batch_size)
+        batch = next(iter(dataset))
+        self.assertIsInstance(batch, tuple)
+
+    def test_create_partition_dataloader_with_transforms_data_type(self) -> None:
+        """Test to verify if the data in the DataLoader batches are of type Tensor."""
+        batch_size = 16
+        dataset = self._create_tensorflow_dataset(batch_size)
+        batch = next(iter(dataset))
+        images = batch[0]
+        self.assertIsInstance(images, tf.Tensor)
+
+    @parameterized.expand([
+        ("not_nan", np.isnan),
+        ("not_inf", np.isinf),
+    ])
+    def test_train_model_loss_value(self, name, condition_func):
+        model = SimpleCNN()
+        model.compile(optimizer='adam',
+                      loss='sparse_categorical_crossentropy',
+                      metrics=['accuracy'])
+
+        dataset = self._create_tensorflow_dataset(16)
+
+        # Perform a single epoch of training
+        history = model.fit(dataset, epochs=1, verbose=0)
+
+        # Fetch the last loss from history
+        last_loss = history.history['loss'][-1]
+
+        # Check if the last loss is NaN or Infinity
+        self.assertFalse(condition_func(last_loss))
+
+
+if __name__ == '__main__':
+    unittest.main()

From b569d2a2c4025735d5209ffd0244771e83b4e0f7 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Thu, 21 Sep 2023 22:59:36 +0200
Subject: [PATCH 04/60] Update FDS docs index (#2337)

---
 datasets/doc/source/index.rst               | 97 +++++++++++++++++++--
 datasets/doc/source/tutorial-quickstart.rst |  2 +-
 2 files changed, 89 insertions(+), 10 deletions(-)

diff --git a/datasets/doc/source/index.rst b/datasets/doc/source/index.rst
index 81a08286b6fd..7b19624b341a 100644
--- a/datasets/doc/source/index.rst
+++ b/datasets/doc/source/index.rst
@@ -1,8 +1,93 @@
-Flower Datasets Documentation
-=============================
+Flower Datasets
+===============
+
+Flower Datasets (``flwr-datasets``) is a library to quickly and easily create datasets for federated
+learning/analytics/evaluation. It is created by the ``Flower Labs`` team that also created `Flower <https://flower.dev>`_ - a Friendly Federated Learning Framework.
+
+Flower Datasets Framework
+-------------------------
+
+Tutorials
+~~~~~~~~~
+
+A learning-oriented series of tutorials is the best place to start.
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Tutorial
+
+   tutorial-quickstart
+
+How-to guides
+~~~~~~~~~~~~~
+
+Problem-oriented how-to guides show step-by-step how to achieve a specific goal.
+
+.. toctree::
+   :maxdepth: 1
+   :caption: How-to guides
+
+   how-to-install-flwr-datasets
+   how-to-use-with-pytorch
+   how-to-use-with-tensorflow
+   how-to-use-with-numpy
+   how-to-disable-enable-progress-bar
+
+References
+~~~~~~~~~~
+
+Information-oriented API reference and other reference material.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: API reference
+
+   ref-api-flwr-datasets
+
+Main features
+-------------
+Flower Datasets library supports:
+
+- **downloading datasets** - choose the dataset from Hugging Face's ``dataset``
+- **partitioning datasets** - customize the partitioning scheme
+- **creating centralized datasets** - leave parts of the dataset unpartitioned (e.g. for centralized evaluation)
 
-Welcome to Flower Datasets' documentation. `Flower <https://flower.dev>`_ is a friendly federated learning framework.
+Thanks to using Hugging Face's ``datasets`` used under the hood, Flower Datasets integrates with the following popular formats/frameworks:
 
+- Hugging Face
+- PyTorch
+- TensorFlow
+- Numpy
+- Pandas
+- Jax
+- Arrow
+
+Install
+-------
+
+The simplest install is
+
+.. code-block:: bash
+
+  python -m pip install flwr-datasets
+
+If you plan to use the image datasets
+
+.. code-block:: bash
+
+  python -m pip install flwr-datasets[vision]
+
+If you plan to use the audio datasets
+
+.. code-block:: bash
+
+  python -m pip install flwr-datasets[audio]
+
+Check out the full details on the download in :doc:`how-to-install-flwr-datasets`.
+
+How To Use the library
+----------------------
+Learn how to use the ``flwr-datasets`` library from the :doc:`tutorial-quickstart` examples .
 
 Join the Flower Community
 -------------------------
@@ -14,9 +99,3 @@ The Flower Community is growing quickly - we're a friendly group of researchers,
     :shadow:
 
     Join us on Slack
-
-
-Flower Datasets
----------------
-
-
diff --git a/datasets/doc/source/tutorial-quickstart.rst b/datasets/doc/source/tutorial-quickstart.rst
index 69d42f16a3b6..d1992b8e68fe 100644
--- a/datasets/doc/source/tutorial-quickstart.rst
+++ b/datasets/doc/source/tutorial-quickstart.rst
@@ -41,7 +41,7 @@ supported by your framework.
 
 Conversion
 ----------
-For more detailed instructions, go to :doc:`how-to`.
+For more detailed instructions, go to :doc:`how-to-use-with-pytorch`.
 
 PyTorch DataLoader
 ^^^^^^^^^^^^^^^^^^

From b63b775b4c35868d33f2b30dc123cc87773a6fc9 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Fri, 22 Sep 2023 16:29:53 +0200
Subject: [PATCH 05/60] Fix default contiguous value in IidPartitioner (#2406)

---------

Co-authored-by: Taner Topal <taner@flower.dev>
---
 .../flwr_datasets/partitioner/iid_partitioner.py     |  2 +-
 .../partitioner/iid_partitioner_test.py              | 12 +++++++++---
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/datasets/flwr_datasets/partitioner/iid_partitioner.py b/datasets/flwr_datasets/partitioner/iid_partitioner.py
index c8dbf8294fec..37b97468cadf 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=True
+            num_shards=self._num_partitions, index=idx, contiguous=False
         )
diff --git a/datasets/flwr_datasets/partitioner/iid_partitioner_test.py b/datasets/flwr_datasets/partitioner/iid_partitioner_test.py
index d89eefeba9f2..5f851807f4bd 100644
--- a/datasets/flwr_datasets/partitioner/iid_partitioner_test.py
+++ b/datasets/flwr_datasets/partitioner/iid_partitioner_test.py
@@ -18,6 +18,7 @@
 import unittest
 from typing import Tuple
 
+import numpy as np
 from parameterized import parameterized
 
 from datasets import Dataset
@@ -100,11 +101,16 @@ def test_load_partition_correct_data(
         self, num_partitions: int, num_rows: int
     ) -> None:
         """Test if the data in partition is equal to the expected."""
-        _, partitioner = _dummy_setup(num_partitions, num_rows)
-        partition_size = num_rows // num_partitions
+        dataset, partitioner = _dummy_setup(num_partitions, num_rows)
         partition_index = 2
         partition = partitioner.load_partition(partition_index)
-        self.assertEqual(partition["features"][0], partition_index * partition_size)
+        row_id = 0
+        self.assertEqual(
+            partition["features"][row_id],
+            dataset[np.arange(partition_index, len(dataset), num_partitions)][
+                "features"
+            ][row_id],
+        )
 
     @parameterized.expand(  # type: ignore
         [

From 2b9da50283b70075b69c1dca22a92ac99a7b35da Mon Sep 17 00:00:00 2001
From: Taner Topal <taner@flower.dev>
Date: Fri, 22 Sep 2023 11:11:29 -0700
Subject: [PATCH 06/60] Fix repository URL for datasets (#2415)

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

diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml
index 6067ff0517db..954441e5d2e4 100644
--- a/datasets/pyproject.toml
+++ b/datasets/pyproject.toml
@@ -10,7 +10,7 @@ license = "Apache-2.0"
 authors = ["The Flower Authors <hello@flower.dev>"]
 readme = "README.md"
 homepage = "https://flower.dev"
-repository = "https://github.com/adap/flower/datasets"
+repository = "https://github.com/adap/flower"
 documentation = "https://flower.dev/docs/datasets"
 keywords = [
     "flower",

From 725176452291d4abe16549d6777e2f7b73e43b80 Mon Sep 17 00:00:00 2001
From: Navin Chandra <98466550+navin772@users.noreply.github.com>
Date: Sat, 23 Sep 2023 02:09:40 +0530
Subject: [PATCH 07/60] Fix video link in colab notebook of
 flower-in-30-minutes (#2414)

---
 examples/flower-in-30-minutes/tutorial.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/flower-in-30-minutes/tutorial.ipynb b/examples/flower-in-30-minutes/tutorial.ipynb
index b1686529c462..336ec4c19644 100644
--- a/examples/flower-in-30-minutes/tutorial.ipynb
+++ b/examples/flower-in-30-minutes/tutorial.ipynb
@@ -23,7 +23,7 @@
     "## Complementary Content\n",
     "\n",
     "But before do so, let me point you to a few video tutorials in the [Flower Youtube channel](https://www.youtube.com/@flowerlabs) that you might want to check out after this tutorial. We post new videos fairly regularly with new content:\n",
-    "* **[VIDEO]** quickstart-tensorflow: [15-min video on how to start with Flower + Tensorflow/Keras](https://www.youtube.com/watch?v=jOmmuzMIQ4c)\n",
+    "* **[VIDEO]** quickstart-tensorflow: [15-min video on how to start with Flower + Tensorflow/Keras](https://www.youtube.com/watch?v=FGTc2TQq7VM)\n",
     "* **[VIDEO]** quickstart-pytorch: [20-min video on how to start with Flower + PyTorch](https://www.youtube.com/watch?v=jOmmuzMIQ4c)\n",
     "* **[VIDEO]**  Flower simulation mini-series: [9 line-by-line video tutorials](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)"
    ]

From dca3102c8ffcec1c9f3194422e3a1a6e211f20b1 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Mon, 25 Sep 2023 14:16:47 +0200
Subject: [PATCH 08/60] Fix code block from python to bash (#2405)

---
 datasets/doc/source/tutorial-quickstart.rst | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/datasets/doc/source/tutorial-quickstart.rst b/datasets/doc/source/tutorial-quickstart.rst
index d1992b8e68fe..8a70ee8854be 100644
--- a/datasets/doc/source/tutorial-quickstart.rst
+++ b/datasets/doc/source/tutorial-quickstart.rst
@@ -5,17 +5,23 @@ Run Flower Datasets as fast as possible by learning only the essentials.
 
 Install Federated Datasets
 --------------------------
-Run on the command line::
+Run on the command line
+
+.. code-block:: bash
 
   python -m pip install flwr-datasets[vision]
 
 Install the ML framework
 ------------------------
-TensorFlow::
+TensorFlow
+
+.. code-block:: bash
 
   pip install tensorflow
 
-PyTorch::
+PyTorch
+
+.. code-block:: bash
 
   pip install torch torchvision
 

From 2fd02c52d881cab7da82ac7c2aea50a02ad33d67 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Mon, 25 Sep 2023 15:40:37 +0200
Subject: [PATCH 09/60] Make Flower Datasets docs publishable (#2418)

---
 .github/workflows/docs.yml | 1 +
 dev/build-docs.sh          | 4 ++++
 2 files changed, 5 insertions(+)

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 52f9a49a259a..f74532dd721c 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -39,3 +39,4 @@ jobs:
           aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./doc/build/html/ s3://flower.dev/docs/framework
           aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./baselines/doc/build/html/ s3://flower.dev/docs/baselines
           aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./examples/doc/build/html/ s3://flower.dev/docs/examples
+          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./datasets/doc/build/html/ s3://flower.dev/docs/datasets
diff --git a/dev/build-docs.sh b/dev/build-docs.sh
index ca57536901b2..c464cf908c87 100755
--- a/dev/build-docs.sh
+++ b/dev/build-docs.sh
@@ -17,3 +17,7 @@ cd examples/doc
 make docs
 
 cd $ROOT
+cd datasets/doc
+make docs
+
+cd $ROOT

From 204a4fee6289266b73afdfeae3e63d7ee9125e9c Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Mon, 25 Sep 2023 15:50:06 +0200
Subject: [PATCH 10/60] Add parameter to actions/checkout to fix doc versions
 (#2419)

---
 .github/workflows/docs.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index f74532dd721c..78b04c5138d4 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -21,6 +21,8 @@ jobs:
     name: Build and deploy
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
       - name: Bootstrap
         uses: ./.github/actions/bootstrap
       - name: Install pandoc

From 9ee473152f2fde2a5b05b99829db27a607dc46ec Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Mon, 25 Sep 2023 22:37:06 +0200
Subject: [PATCH 11/60] Add new wheel building job and use it in E2E tests
 (#2291)

---
 .github/workflows/e2e.yml | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 3b70db43a6c8..214c8579d450 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -16,9 +16,39 @@ env:
   FLWR_TELEMETRY_ENABLED: 0
 
 jobs:
+  wheel:
+    runs-on: ubuntu-22.04
+    name: Build, test and upload wheel
+    steps:
+      - uses: actions/checkout@v3
+      - name: Bootstrap
+        uses: ./.github/actions/bootstrap
+      - name: Install dependencies (mandatory only)
+        run: python -m poetry install
+      - name: Build wheel
+        run: ./dev/build.sh
+      - name: Test wheel
+        run: ./dev/test-wheel.sh
+      - name: Upload wheel
+        id: upload
+        env:
+          AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
+          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
+        run: |
+          cd ./dist
+          echo "WHL_PATH=$(ls *.whl)" >> "$GITHUB_OUTPUT"
+          sha_short=$(git rev-parse --short HEAD)
+          echo "SHORT_SHA=$sha_short" >> "$GITHUB_OUTPUT"
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://artifact.flower.dev/py/${{ github.head_ref }}/$sha_short --recursive
+    outputs:
+      whl_path: ${{ steps.upload.outputs.WHL_PATH }}
+      short_sha: ${{ steps.upload.outputs.SHORT_SHA }}
+
   frameworks:
     runs-on: ubuntu-22.04
     timeout-minutes: 10
+    needs: wheel
     # Using approach described here:
     # https://docs.github.com/en/actions/using-jobs/using-a-matrix-for-your-jobs
     strategy:
@@ -89,6 +119,9 @@ jobs:
           python-version: 3.8
       - name: Install dependencies
         run: python -m poetry install
+      - name: Install Flower wheel from artifact store
+        run: |
+          python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Download dataset
         if: ${{ matrix.dataset }}
         run: python -c "${{ matrix.dataset }}"
@@ -102,6 +135,7 @@ jobs:
   strategies:
     runs-on: ubuntu-22.04
     timeout-minutes: 10
+    needs: wheel
     strategy:
       matrix:
         strat: ["FedMedian", "FedTrimmedAvg", "QFedAvg", "FaultTolerantFedAvg", "FedAvgM", "FedAdam", "FedAdagrad", "FedYogi"]
@@ -119,6 +153,9 @@ jobs:
       - name: Install dependencies
         run: |
           python -m poetry install
+      - name: Install Flower wheel from artifact store
+        run: |
+          python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Cache Datasets
         uses: actions/cache@v3
         with:

From 4a8ac498c9cc64178fe3fb1f3b333ffb9057fedd Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Thu, 28 Sep 2023 01:43:07 +0100
Subject: [PATCH 12/60] Adapt secaggplus-mt to use workload_id (#2408)

---
 examples/secaggplus-mt/driver.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/examples/secaggplus-mt/driver.py b/examples/secaggplus-mt/driver.py
index c168edf070af..4e0a53ed1c91 100644
--- a/examples/secaggplus-mt/driver.py
+++ b/examples/secaggplus-mt/driver.py
@@ -23,7 +23,7 @@ def merge(_task: task_pb2.Task, _merge_task: task_pb2.Task) -> task_pb2.Task:
         task_pb2.TaskIns(
             task_id="",  # Do not set, will be created and set by the DriverAPI
             group_id="",
-            workload_id="",
+            workload_id=workload_id,
             task=merge(
                 task,
                 task_pb2.Task(
@@ -84,8 +84,14 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 
 # -------------------------------------------------------------------------- Driver SDK
 driver.connect()
+create_workload_res: driver_pb2.CreateWorkloadResponse = driver.create_workload(
+    req=driver_pb2.CreateWorkloadRequest()
+)
 # -------------------------------------------------------------------------- Driver SDK
 
+workload_id = create_workload_res.workload_id
+print(f"Created workload id {workload_id}")
+
 history = History()
 for server_round in range(num_rounds):
     print(f"Commencing server round {server_round + 1}")
@@ -113,7 +119,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
         # loop and wait until enough client nodes are available.
         while True:
             # Get a list of node ID's from the server
-            get_nodes_req = driver_pb2.GetNodesRequest()
+            get_nodes_req = driver_pb2.GetNodesRequest(workload_id=workload_id)
 
             # ---------------------------------------------------------------------- Driver SDK
             get_nodes_res: driver_pb2.GetNodesResponse = driver.get_nodes(
@@ -121,7 +127,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
             )
             # ---------------------------------------------------------------------- Driver SDK
 
-            all_node_ids: List[int] = get_nodes_res.node_ids
+            all_node_ids: List[int] = [node.node_id for node in get_nodes_res.nodes]
 
             if len(all_node_ids) >= num_client_nodes_per_round:
                 # Sample client nodes

From e63229cb5e87a7e07cfc18ca44a95e6d4d8e4655 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 29 Sep 2023 14:13:49 +0100
Subject: [PATCH 13/60] Use minimal ray package (#2402)

---
 doc/source/ref-changelog.md | 2 +-
 pyproject.toml              | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index e1d90b01fb35..ced58029bd5a 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -20,7 +20,7 @@
 
 - **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331))
 
-- **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))
+- **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))
 
   Flower received many improvements under the hood, too many to list here.
 
diff --git a/pyproject.toml b/pyproject.toml
index 91d7d810f810..dfdd75ba11ab 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -66,7 +66,7 @@ cryptography = "^41.0.2"
 pycryptodome = "^3.18.0"
 iterators = "^0.0.2"
 # Optional dependencies (VCE)
-ray = { version = "==2.6.3", extras = ["default"], optional = true }
+ray = { version = "==2.6.3", optional = true }
 pydantic = { version = "<2.0.0", optional = true }
 # Optional dependencies (REST transport layer)
 requests = { version = "^2.31.0", optional = true }

From ab7f77584b7caf37e98752016249b0cbfc6abd79 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Sat, 30 Sep 2023 20:05:04 +0200
Subject: [PATCH 14/60] Make videos bigger to allow for index (#2436)

---
 doc/source/index.rst | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/doc/source/index.rst b/doc/source/index.rst
index 48f8d59ea9b7..4ac99cc24c09 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -61,17 +61,15 @@ A learning-oriented series of federated learning tutorials, the best place to st
 
 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>`
 
-.. grid:: 2
+We also made video tutorials for PyTorch:
 
-  .. grid-item-card::  PyTorch
+..  youtube:: jOmmuzMIQ4c
+   :width: 80%
 
-    ..  youtube:: jOmmuzMIQ4c
-       :width: 100%
+And TensorFlow:
 
-  .. grid-item-card::  TensorFlow
-
-    ..  youtube:: FGTc2TQq7VM
-       :width: 100%
+..  youtube:: FGTc2TQq7VM
+   :width: 80%
 
 How-to guides
 ~~~~~~~~~~~~~

From 2997b79b9d331f8b0e83dbcb387f41f47eaf11c7 Mon Sep 17 00:00:00 2001
From: Taner Topal <taner@flower.dev>
Date: Wed, 4 Oct 2023 06:06:21 -0700
Subject: [PATCH 15/60] Set AWS_REGION correctly for CI job (#2454)

---
 .github/workflows/e2e.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 214c8579d450..74e9b4c2684e 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -32,7 +32,7 @@ jobs:
       - name: Upload wheel
         id: upload
         env:
-          AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
+          AWS_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
           AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
         run: |

From a6105e73a7c8aa7e2964b21629b64c1e2acfc84e Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 4 Oct 2023 16:22:15 +0200
Subject: [PATCH 16/60] Remove duplicate workflow (#2445)

---
 .github/workflows/release.yml | 26 --------------------------
 1 file changed, 26 deletions(-)
 delete mode 100644 .github/workflows/release.yml

diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
deleted file mode 100644
index d8f4e403482b..000000000000
--- a/.github/workflows/release.yml
+++ /dev/null
@@ -1,26 +0,0 @@
-name: Release
-
-on:
-  schedule:
-    - cron: "0 23 * * *"
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-env:
-  FLWR_TELEMETRY_ENABLED: 0
-
-jobs:
-  nightly_release:
-    runs-on: ubuntu-22.04
-    name: Nightly
-    steps:
-      - uses: actions/checkout@v4
-      - name: Bootstrap
-        uses: ./.github/actions/bootstrap
-      - name: Release nightly
-        env:
-          PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
-        run: |
-          ./dev/publish-nightly.sh

From 2a7f62709411f74b8de206a920b5ecf4fb54baec Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Thu, 5 Oct 2023 09:22:11 +0200
Subject: [PATCH 17/60] Revert to AWS_DEFAULT_REGION (#2459)

---
 .github/workflows/e2e.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 74e9b4c2684e..214c8579d450 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -32,7 +32,7 @@ jobs:
       - name: Upload wheel
         id: upload
         env:
-          AWS_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
+          AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
           AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
         run: |

From b02d0420281421dbe6413ea82d9e41e8b2a5608a Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Thu, 5 Oct 2023 09:26:38 +0200
Subject: [PATCH 18/60] Update android-kotlin example requirements (#2462)

---
 examples/android-kotlin/pyproject.toml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/android-kotlin/pyproject.toml b/examples/android-kotlin/pyproject.toml
index dee6cbc35711..9cf0688d83b5 100644
--- a/examples/android-kotlin/pyproject.toml
+++ b/examples/android-kotlin/pyproject.toml
@@ -1,3 +1,7 @@
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
 [tool.poetry]
 name = "flower-android-kotlin"
 version = "0.1.0"
@@ -7,7 +11,3 @@ authors = ["Steven Hé (Sīchàng) <sichang.he@dukekunshan.edu.cn>"]
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

From afca860e3f73c74fa90cd40846c0dd6087619ff0 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Thu, 5 Oct 2023 09:33:06 +0200
Subject: [PATCH 19/60] Update mt-pytorch example requirements (#2463)

---
 examples/mt-pytorch/pyproject.toml   | 7 +++----
 examples/mt-pytorch/requirements.txt | 4 ++--
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/examples/mt-pytorch/pyproject.toml b/examples/mt-pytorch/pyproject.toml
index f285af016499..4978035495ea 100644
--- a/examples/mt-pytorch/pyproject.toml
+++ b/examples/mt-pytorch/pyproject.toml
@@ -10,8 +10,7 @@ authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr-nightly = { version = "^1.5.0.dev20230629", extras = ["simulation", "rest"] }
-# flwr = { path = "../../", develop = true, extras = ["simulation", "rest"] }
-torch = "^2.0.1"
-torchvision = "^0.15.2"
+flwr-nightly = {version = ">=1.0,<2.0", extras = ["rest", "simulation"]}
+torch = "1.13.1"
+torchvision = "0.14.1"
 tqdm = "4.65.0"
diff --git a/examples/mt-pytorch/requirements.txt b/examples/mt-pytorch/requirements.txt
index 98b7617e776d..ae0a65386f2b 100644
--- a/examples/mt-pytorch/requirements.txt
+++ b/examples/mt-pytorch/requirements.txt
@@ -1,4 +1,4 @@
-flwr-nightly[simulation,rest]
+flwr-nightly[rest,simulation]>=1.0, <2.0
 torch==1.13.1
-torchvision==0.13.0
+torchvision==0.14.1
 tqdm==4.65.0

From e31d48860aec3345d5a6fb69585803eec908e249 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Thu, 5 Oct 2023 09:54:17 +0200
Subject: [PATCH 20/60] Update iOS example requirements (#2461)

---
 examples/ios/pyproject.toml   | 2 +-
 examples/ios/requirements.txt | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/examples/ios/pyproject.toml b/examples/ios/pyproject.toml
index 531e9253e0d1..c1bdbb815bd5 100644
--- a/examples/ios/pyproject.toml
+++ b/examples/ios/pyproject.toml
@@ -10,4 +10,4 @@ authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr = "^1.0.0"
+flwr = ">=1.0,<2.0"
diff --git a/examples/ios/requirements.txt b/examples/ios/requirements.txt
index 9d6b364ee36c..236ca6a487fa 100644
--- a/examples/ios/requirements.txt
+++ b/examples/ios/requirements.txt
@@ -1,2 +1 @@
-flwr~=1.4.0
-numpy~=1.21.1
+flwr>=1.0, <2.0

From ff4a475b4f18d32c73bc3d804b2e0d187fb482f7 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Thu, 5 Oct 2023 09:59:46 +0200
Subject: [PATCH 21/60] Only run publishing job on main repo (#2444)

---
 .github/workflows/android-release.yml   | 1 +
 .github/workflows/docs.yml              | 2 +-
 .github/workflows/e2e.yml               | 3 +++
 .github/workflows/flower-swift_sync.yml | 1 +
 .github/workflows/release-nightly.yml   | 1 +
 .github/workflows/swift.yml             | 2 +-
 6 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/android-release.yml b/.github/workflows/android-release.yml
index 35df8c8a9cfb..ba11e1ee85e7 100644
--- a/.github/workflows/android-release.yml
+++ b/.github/workflows/android-release.yml
@@ -15,6 +15,7 @@ jobs:
         run:
             working-directory: src/kotlin
     name: Release build and publish
+    if: github.repository == 'adap/flower'
     runs-on: ubuntu-latest
     steps:
       - name: Check out code
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 78b04c5138d4..aa267bd9d1ac 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -32,7 +32,7 @@ jobs:
       - name: Build docs
         run: ./dev/build-docs.sh
       - name: Deploy docs
-        if: github.ref == 'refs/heads/main'
+        if: github.ref == 'refs/heads/main' && github.repository == 'adap/flower'
         env:
           AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 214c8579d450..f87db59773b6 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -19,6 +19,7 @@ jobs:
   wheel:
     runs-on: ubuntu-22.04
     name: Build, test and upload wheel
+    if: github.repository == 'adap/flower'
     steps:
       - uses: actions/checkout@v3
       - name: Bootstrap
@@ -120,6 +121,7 @@ jobs:
       - name: Install dependencies
         run: python -m poetry install
       - name: Install Flower wheel from artifact store
+        if: github.repository == 'adap/flower'
         run: |
           python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Download dataset
@@ -154,6 +156,7 @@ jobs:
         run: |
           python -m poetry install
       - name: Install Flower wheel from artifact store
+        if: github.repository == 'adap/flower'
         run: |
           python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Cache Datasets
diff --git a/.github/workflows/flower-swift_sync.yml b/.github/workflows/flower-swift_sync.yml
index d3fce3b22a0f..836d905b2df2 100644
--- a/.github/workflows/flower-swift_sync.yml
+++ b/.github/workflows/flower-swift_sync.yml
@@ -12,6 +12,7 @@ concurrency:
 jobs:
   build:
     runs-on: ubuntu-latest
+    if: github.repository == 'adap/flower'
     steps:
       - uses: actions/checkout@v4
       - name: Pushes src/swift to flower-swift repository
diff --git a/.github/workflows/release-nightly.yml b/.github/workflows/release-nightly.yml
index 0ae9c43ddbf1..823ff1513790 100644
--- a/.github/workflows/release-nightly.yml
+++ b/.github/workflows/release-nightly.yml
@@ -11,6 +11,7 @@ jobs:
   release_nightly:
     runs-on: ubuntu-22.04
     name: Nightly
+    if: github.repository == 'adap/flower'
     steps:
       - uses: actions/checkout@v4
       - name: Bootstrap
diff --git a/.github/workflows/swift.yml b/.github/workflows/swift.yml
index 207bb1283739..8eb9d88575f3 100644
--- a/.github/workflows/swift.yml
+++ b/.github/workflows/swift.yml
@@ -40,7 +40,7 @@ jobs:
 
   deploy_docs:
     needs: "build_docs"
-    if: github.ref == 'refs/heads/main'
+    if: github.ref == 'refs/heads/main' && github.repository == 'adap/flower'
     runs-on: macos-latest
     name: Deploy docs 
     steps:

From cc7db6ee340a070fb343eeb347166ec4236b4e2e Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Thu, 5 Oct 2023 10:57:38 +0200
Subject: [PATCH 22/60] Build wheel but skip upload on forks (#2468)

---
 .github/workflows/e2e.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index f87db59773b6..6fa24006b601 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -19,7 +19,6 @@ jobs:
   wheel:
     runs-on: ubuntu-22.04
     name: Build, test and upload wheel
-    if: github.repository == 'adap/flower'
     steps:
       - uses: actions/checkout@v3
       - name: Bootstrap
@@ -31,6 +30,7 @@ jobs:
       - name: Test wheel
         run: ./dev/test-wheel.sh
       - name: Upload wheel
+        if: github.repository == 'adap/flower'
         id: upload
         env:
           AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}

From 5f504a91f4a9c0c93b0f8255e55c48f777ae8b41 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Thu, 5 Oct 2023 13:24:56 +0200
Subject: [PATCH 23/60] Update the Opacus example requirements (#2469)

---
 examples/opacus/pyproject.toml   | 10 ++++------
 examples/opacus/requirements.txt |  7 +++----
 2 files changed, 7 insertions(+), 10 deletions(-)

diff --git a/examples/opacus/pyproject.toml b/examples/opacus/pyproject.toml
index 8ee2cc7d10b8..af0eaf596fbf 100644
--- a/examples/opacus/pyproject.toml
+++ b/examples/opacus/pyproject.toml
@@ -9,9 +9,7 @@ description = "Differentially Private Federated Learning with Opacus and Flower"
 authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
-python = "^3.8"
-flwr = "^1.0.0"
-# flwr = { path = "../../", develop = true }  # Development
-opacus = "^1.4.0"
-torch = "^1.13.1"
-torchvision = "^0.14.0"
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+opacus = "1.4.0"
+torchvision = "0.15.2"
diff --git a/examples/opacus/requirements.txt b/examples/opacus/requirements.txt
index e6e5dbb2fdfa..f17b78fbf311 100644
--- a/examples/opacus/requirements.txt
+++ b/examples/opacus/requirements.txt
@@ -1,4 +1,3 @@
-flwr~=1.4.0
-numpy~=1.21.1
-torch~=2.0.1
-torchvision~=0.15.2
+flwr>=1.0, <2.0
+opacus==1.4.0
+torchvision==0.15.2

From 990f59c5aeafbfce4e9606c73fb8d90505fe279b Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Thu, 5 Oct 2023 13:30:19 +0200
Subject: [PATCH 24/60] Update jax example requirements (#2466)

---
 examples/quickstart-jax/pyproject.toml   | 11 +++++------
 examples/quickstart-jax/requirements.txt |  8 ++++----
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/examples/quickstart-jax/pyproject.toml b/examples/quickstart-jax/pyproject.toml
index 6a67cff6f4b5..41b4462d0a14 100644
--- a/examples/quickstart-jax/pyproject.toml
+++ b/examples/quickstart-jax/pyproject.toml
@@ -5,12 +5,11 @@ description = "JAX example training a linear regression model with federated lea
 authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
-python = "^3.8"
-flwr = "^1.0.0"
-jax = "^0.4.0"
-jaxlib = "^0.4.0"
-scikit-learn = "^1.1.1"
-numpy = "^1.21.4"
+python = ">=3.8,<3.11"
+flwr = "1.0.0"
+jax = "0.4.17"
+jaxlib = "0.4.17"
+scikit-learn = "1.1.1"
 
 [build-system]
 requires = ["poetry-core>=1.4.0"]
diff --git a/examples/quickstart-jax/requirements.txt b/examples/quickstart-jax/requirements.txt
index bf7a9c64d66f..964f07a51b7d 100644
--- a/examples/quickstart-jax/requirements.txt
+++ b/examples/quickstart-jax/requirements.txt
@@ -1,4 +1,4 @@
-flwr~=1.4.0
-jax~=0.4.10
-numpy~=1.21.1
-scikit_learn~=1.2.2
+flwr>=1.0,<2.0
+jax==0.4.17
+jaxlib==0.4.17
+scikit-learn==1.1.1

From f6ed52b6e4e3cd5cbf15db8d256e230f930e76cc Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Fri, 6 Oct 2023 10:31:37 +0200
Subject: [PATCH 25/60] Update advanced-pytorch requirements (#2471)

---
 examples/advanced-pytorch/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/advanced-pytorch/requirements.txt b/examples/advanced-pytorch/requirements.txt
index 21c886d16e4d..ba7b284df90e 100644
--- a/examples/advanced-pytorch/requirements.txt
+++ b/examples/advanced-pytorch/requirements.txt
@@ -1,4 +1,4 @@
 flwr>=1.0, <2.0
 torch==1.13.1
 torchvision==0.14.1
-
+validators==0.18.2

From de207b364940125c102a12a4b53ad4c20c646649 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Fri, 6 Oct 2023 10:35:46 +0200
Subject: [PATCH 26/60] Update android example requirements (#2472)

---
 examples/android/pyproject.toml   | 1 -
 examples/android/requirements.txt | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/examples/android/pyproject.toml b/examples/android/pyproject.toml
index 0ecaaa73989f..2b9cd8c978a7 100644
--- a/examples/android/pyproject.toml
+++ b/examples/android/pyproject.toml
@@ -10,7 +10,6 @@ authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-# flwr = { path = "../../", develop = true }  # Development
 flwr = ">=1.0,<2.0"
 tensorflow-cpu = {version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "platform_machine == \"x86_64\""}
 tensorflow-macos = {version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "sys_platform == \"darwin\" and platform_machine == \"arm64\""}
diff --git a/examples/android/requirements.txt b/examples/android/requirements.txt
index 6420aab25ec8..7a70c46a8128 100644
--- a/examples/android/requirements.txt
+++ b/examples/android/requirements.txt
@@ -1,3 +1,3 @@
 flwr>=1.0, <2.0
-tensorflow-macos>=2.9.1, != 2.11.1 ; sys_platform == "darwin" and platform_machine == "arm64"
 tensorflow-cpu>=2.9.1, != 2.11.1 ; platform_machine == "x86_64"
+tensorflow-macos>=2.9.1, != 2.11.1 ; sys_platform == "darwin" and platform_machine == "arm64"

From c236d14ed7a2f73d773032135b3bdc01ebc46415 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Fri, 6 Oct 2023 10:40:04 +0200
Subject: [PATCH 27/60] Update advanced-tensorflow example requirements (#2470)

---
 examples/advanced-tensorflow/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/advanced-tensorflow/requirements.txt b/examples/advanced-tensorflow/requirements.txt
index 6420aab25ec8..7a70c46a8128 100644
--- a/examples/advanced-tensorflow/requirements.txt
+++ b/examples/advanced-tensorflow/requirements.txt
@@ -1,3 +1,3 @@
 flwr>=1.0, <2.0
-tensorflow-macos>=2.9.1, != 2.11.1 ; sys_platform == "darwin" and platform_machine == "arm64"
 tensorflow-cpu>=2.9.1, != 2.11.1 ; platform_machine == "x86_64"
+tensorflow-macos>=2.9.1, != 2.11.1 ; sys_platform == "darwin" and platform_machine == "arm64"

From 56b7d79fb9a5532c3af028887de408e7fa8e4a21 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Fri, 6 Oct 2023 11:20:41 +0200
Subject: [PATCH 28/60] Skip publishing jobs on fork PRs (#2476)

---
 .github/workflows/docs.yml  | 2 +-
 .github/workflows/e2e.yml   | 6 +++---
 .github/workflows/swift.yml | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index aa267bd9d1ac..da3a67fc155e 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -32,7 +32,7 @@ jobs:
       - name: Build docs
         run: ./dev/build-docs.sh
       - name: Deploy docs
-        if: github.ref == 'refs/heads/main' && github.repository == 'adap/flower'
+        if: github.ref == 'refs/heads/main' && github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
         env:
           AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 6fa24006b601..3a58503ea66e 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -30,7 +30,7 @@ jobs:
       - name: Test wheel
         run: ./dev/test-wheel.sh
       - name: Upload wheel
-        if: github.repository == 'adap/flower'
+        if: github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
         id: upload
         env:
           AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
@@ -121,7 +121,7 @@ jobs:
       - name: Install dependencies
         run: python -m poetry install
       - name: Install Flower wheel from artifact store
-        if: github.repository == 'adap/flower'
+        if: github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
         run: |
           python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Download dataset
@@ -156,7 +156,7 @@ jobs:
         run: |
           python -m poetry install
       - name: Install Flower wheel from artifact store
-        if: github.repository == 'adap/flower'
+        if: github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
         run: |
           python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Cache Datasets
diff --git a/.github/workflows/swift.yml b/.github/workflows/swift.yml
index 8eb9d88575f3..9edd7f7ff6e1 100644
--- a/.github/workflows/swift.yml
+++ b/.github/workflows/swift.yml
@@ -40,7 +40,7 @@ jobs:
 
   deploy_docs:
     needs: "build_docs"
-    if: github.ref == 'refs/heads/main' && github.repository == 'adap/flower'
+    if: github.ref == 'refs/heads/main' && github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
     runs-on: macos-latest
     name: Deploy docs 
     steps:

From 7e14c96e1577a4a5fa9db5e56a2286bc569dc556 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Fri, 6 Oct 2023 12:01:41 +0200
Subject: [PATCH 29/60] Add script that generates the changelog for the latest
 version (#2451)

---------

Co-authored-by: Taner Topal <taner@flower.dev>
---
 dev/get-latest-changelog.sh | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)
 create mode 100755 dev/get-latest-changelog.sh

diff --git a/dev/get-latest-changelog.sh b/dev/get-latest-changelog.sh
new file mode 100755
index 000000000000..d7f4ca7db168
--- /dev/null
+++ b/dev/get-latest-changelog.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
+
+# Extract the latest release notes from the changelog, which starts at the line containing 
+# the latest version tag and ends one line before the previous version tag.
+tags=$(git tag --sort=-creatordate)
+new_version=$(echo "$tags" | sed -n '1p')
+old_version=$(echo "$tags" | sed -n '2p')
+
+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

From fa0fd8a465a489ece7ef2b585bbbc19b9857e1f1 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 6 Oct 2023 14:59:24 +0100
Subject: [PATCH 30/60] Update `embedded-devices` example (#2384)

---
 doc/source/ref-changelog.md                   |   2 +
 examples/embedded-devices/Dockerfile          |  31 +--
 examples/embedded-devices/README.md           | 208 +++++++++++------
 examples/embedded-devices/_static/diagram.png | Bin 358110 -> 84958 bytes
 .../embedded-devices/_static/rpi_imager.png   | Bin 0 -> 259665 bytes
 .../embedded-devices/base_images/README.md    |  11 -
 .../base_images/cpu/Dockerfile                |  43 ----
 .../embedded-devices/base_images/cpu/build.sh |   9 -
 .../base_images/gpu/Dockerfile                |  10 -
 .../embedded-devices/base_images/gpu/build.sh |   9 -
 examples/embedded-devices/build_image.sh      |  12 -
 .../build_jetson_flower_client.sh             |  42 ++++
 examples/embedded-devices/client.py           | 194 ----------------
 examples/embedded-devices/client_pytorch.py   | 216 ++++++++++++++++++
 examples/embedded-devices/client_tf.py        | 133 +++++++++++
 examples/embedded-devices/requirements.txt    |   4 -
 .../embedded-devices/requirements_pytorch.txt |   4 +
 examples/embedded-devices/requirements_tf.txt |   2 +
 examples/embedded-devices/run_jetson.sh       |  25 --
 examples/embedded-devices/run_pi.sh           |  25 --
 examples/embedded-devices/server.py           | 162 ++++---------
 examples/embedded-devices/utils.py            | 175 --------------
 22 files changed, 587 insertions(+), 730 deletions(-)
 create mode 100644 examples/embedded-devices/_static/rpi_imager.png
 delete mode 100644 examples/embedded-devices/base_images/README.md
 delete mode 100644 examples/embedded-devices/base_images/cpu/Dockerfile
 delete mode 100755 examples/embedded-devices/base_images/cpu/build.sh
 delete mode 100644 examples/embedded-devices/base_images/gpu/Dockerfile
 delete mode 100755 examples/embedded-devices/base_images/gpu/build.sh
 delete mode 100755 examples/embedded-devices/build_image.sh
 create mode 100755 examples/embedded-devices/build_jetson_flower_client.sh
 delete mode 100644 examples/embedded-devices/client.py
 create mode 100644 examples/embedded-devices/client_pytorch.py
 create mode 100644 examples/embedded-devices/client_tf.py
 delete mode 100644 examples/embedded-devices/requirements.txt
 create mode 100644 examples/embedded-devices/requirements_pytorch.txt
 create mode 100644 examples/embedded-devices/requirements_tf.txt
 delete mode 100755 examples/embedded-devices/run_jetson.sh
 delete mode 100755 examples/embedded-devices/run_pi.sh
 delete mode 100644 examples/embedded-devices/utils.py

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index ced58029bd5a..86993542ea4d 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -16,6 +16,8 @@
 
   - FedProx ([#2210](https://github.com/adap/flower/pull/2210), [#2286](https://github.com/adap/flower/pull/2286))
 
+- **Update Flower Examples** ([#2384](https://github.com/adap/flower/pull/2384))
+
 - **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))
 
 - **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331))
diff --git a/examples/embedded-devices/Dockerfile b/examples/embedded-devices/Dockerfile
index add8d6d50d2e..ea63839bc9d6 100644
--- a/examples/embedded-devices/Dockerfile
+++ b/examples/embedded-devices/Dockerfile
@@ -1,28 +1,13 @@
-ARG BASE_IMAGE_TYPE=cpu
-# these images have been pushed to Dockerhub but you can find
-# each Dockerfile used in the `base_images` directory 
-FROM jafermarq/jetsonfederated_$BASE_IMAGE_TYPE:latest
+ARG BASE_IMAGE
 
-RUN apt-get install wget -y
+# Pull the base image from NVIDIA
+FROM $BASE_IMAGE
 
-# Download and extract CIFAR-10
-# To keep things simple, we keep this as part of the docker image.
-# If the dataset is already in your system you can mount it instead.
-ENV DATA_DIR=/app/data/cifar-10
-RUN mkdir -p $DATA_DIR
-WORKDIR $DATA_DIR
-RUN wget https://www.cs.toronto.edu/\~kriz/cifar-10-python.tar.gz 
-RUN tar -zxvf cifar-10-python.tar.gz
-
-WORKDIR /app
-# Scripts needed for Flower client
-ADD client.py /app
-ADD utils.py /app
-
-# update pip
+# Update pip
 RUN pip3 install --upgrade pip
 
-# making sure the latest version of flower is installed
-RUN pip3 install flwr>=1.0.0
+# Install flower
+RUN pip3 install flwr>=1.0
+RUN pip3 install tqdm==4.65.0
 
-ENTRYPOINT ["python3","-u","./client.py"]
+WORKDIR /client
diff --git a/examples/embedded-devices/README.md b/examples/embedded-devices/README.md
index 16cc47bf3992..b485f663e08f 100644
--- a/examples/embedded-devices/README.md
+++ b/examples/embedded-devices/README.md
@@ -1,142 +1,216 @@
 # Federated Learning on Embedded Devices with Flower
 
-This demo will show you how Flower makes it very easy to run Federated Learning workloads on edge devices. Here we'll be showing how to use NVIDIA Jetson devices and Raspberry Pi as Flower clients. This demo uses Flower with PyTorch. The source code used is mostly borrowed from the [example that Flower provides for CIFAR-10](https://github.com/adap/flower/tree/main/src/py/flwr_example/pytorch_cifar).
+This example will show you how Flower makes it very easy to run Federated Learning workloads on edge devices. Here we'll be showing how to use NVIDIA Jetson devices and Raspberry Pi as Flower clients. You can run this example using either PyTorch or Tensorflow. The FL workload (i.e. model, dataset and training loop) is mostly borrowed from the [quickstart-pytorch](https://github.com/adap/flower/tree/main/examples/simulation-pytorch) and [quickstart-tensorflow](https://github.com/adap/flower/tree/main/examples/quickstart-tensorflow) examples.
 
-## Getting things ready
+![Different was of running Flower FL on embedded devices](_static/diagram.png)
 
-This is a list of components that you'll need:
+## Getting things ready
 
-- For server: A machine running Linux/macOS.
-- For clients: either a Rapsberry Pi 3 B+ (RPi 4 would work too) or a Jetson Xavier-NX (or any other recent NVIDIA-Jetson device).
-- A 32GB uSD card and ideally UHS-1 or better. (not needed if you plan to use a Jetson TX2 instead)
-- Software to flash the images to a uSD card (e.g. [Etcher](https://www.balena.io/etcher/))
+> This example is designed for beginners that know a bit about Flower and/or ML but that are less familiar with embedded devices. If you already have a couple of devices up and running, clone this example and start the Flower clients after launching the Flower server.
 
-What follows is a step-by-step guide on how to setup your client/s and the server. In order to minimize the amount of setup and potential issues that might arise due to the hardware/software heterogenity between clients we'll be running the clients inside a Docker. We provide two docker images: one built for Jetson devices and make use of their GPU; and the other for CPU-only training suitable for Raspberry Pi (but would also work on Jetson devices). The following diagram illustrates the setup for this demo:
+This tutorial allows for a variety of settings (some shown in the diagrams above). As long as you have access to one embedded device, you can follow along. This is a list of components that you'll need:
 
-<!-- jetson xavier-nx image borrowed from: https://developer.nvidia.com/embedded/jetson-xavier-nx-devkit -->
+- For Flower server: A machine running Linux/macOS/Windows (e.g. your laptop). You can run the server on an embedded device too!
+- For Flower clients (one or more): Raspberry Pi 4 (or Zero 2), or an NVIDIA Jetson Xavier-NX (or Nano), or anything similar to these.
+- A uSD card with 32GB or more.
+- Software to flash the images to a uSD card:
+  - For Raspberry Pi we recommend the [Raspberry Pi Imager](https://www.raspberrypi.com/software/)
+  - For other devices [balenaEtcher](https://www.balena.io/etcher/) it's a great option.
 
-![alt text](_static/diagram.png)
+What follows is a step-by-step guide on how to setup your client/s and the server.
 
-## Clone this repo
+## Clone this example
 
-Start with cloning the Flower repo and checking out the example. We have prepared a single line which you can copy into your shell:
+Start with cloning this example on your laptop or desktop machine. Later you'll run the same command on your embedded devices. We have prepared a single line which you can copy and execute:
 
 ```bash
-$ git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/embedded-devices . && rm -rf flower && cd embedded-devices
+git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/embedded-devices . && rm -rf flower && cd embedded-devices
 ```
 
 ## Setting up the server
 
-The only requirement for the server is to have flower installed. You can do so by running `pip install flwr` inside your virtualenv or conda environment.
+The only requirement for the server is to have Flower installed alongside your ML framework of choice. Inside your Python environment run:
+
+```bash
+pip install -r requierments_pytorch.txt # to install Flower and PyTorch
+
+# or the below for TensorFlower
+# pip install -r requirements_tensorflow.txt
+```
+
+If you are working on this tutorial on your laptop or desktop, it can host the Flower server that will orchestrate the entire FL process. You could also use an embedded device (e.g. a Raspberry Pi) as the Flower server. In order to do that, please follow the setup steps below.
+
+## Setting up a Raspberry Pi
+
+> Wheter you use your RPi as a Flower server or a client, you need to follow these steps.
+
+![alt text](_static/rpi_imager.png)
+
+1. **Installing Ubuntu server on your Raspberry Pi** is easy with the [Raspberry Pi Imager](https://www.raspberrypi.com/software/). Before starting ensure you have a uSD card attached to your PC/Laptop and that it has sufficient space (ideally larger than 16GB). Then:
+
+   - Click on `CHOOSE OS` > `Other general-pupose OS` > `Ubuntu` > `Ubuntu Server 22.04.03 LTS (64-bit)`. Other versions of `Ubuntu Server` would likely work but try to use a `64-bit` one.
+   - Select the uSD you want to flash the OS onto. (This will be the uSD you insert in your Raspberry Pi)
+   - Click on the gear icon on the bottom right of the `Raspberry Pi Imager` window (the icon only appears after choosing your OS image). Here you can very conveniently set the username/password to access your device over ssh. You'll see I use as username `piubuntu` (you can choose something different) It's also the ideal place to select your WiFi network and add the password (this is of course not needed if you plan to connect the Raspberry Pi via ethernet). Click "save" when you are done.
+   - Finally, click on `WRITE` to start flashing Ubuntu onto the uSD card.
+
+2. **Connecting to your Rapsberry Pi**
+
+   After `ssh`-ing into your Raspberry Pi for the first time, make sure your OS is up-to-date.
+
+   - Run: `sudo apt update` to look for updates
+   - And then: `sudo apt upgrade -y` to apply updates (this might take a few minutes on the RPi Zero)
+   - Then reboot your RPi with `sudo reboot`. Then ssh into it again.
+
+3. **Preparations for your Flower experiments**
+
+   - Install `pip`. In the terminal type: `sudo apt install python3-pip -y`
+   - Now clone this directory. You just need to execute the `git clone` command shown at the top of this README.md on your device.
+   - Install Flower and your ML framework: We have prepared some convenient installation scripts that will install everything you need. You are free to install other versions of these ML frameworks to suit your needs.
+     - If you want your clients to use PyTorch: `pip3 install -r requirements_pytorch.txt`
+     - If you want your clients to use TensorFlow: `pip3 install -r requirements_tf.txt`
+
+   > While preparing this example I noticed that installing TensorFlow on the **Raspberry pi Zero** would fail due to lack of RAM (it only has 512MB). A workaround is to create a `swap` disk partition (non-existant by default) so the OS can offload some elements to disk. I followed the steps described [in this blogpost](https://www.digitalocean.com/community/tutorials/how-to-add-swap-space-on-ubuntu-20-04) that I copy below. You can follow these steps if you often see your RPi Zero running out of memory:
+
+   ```bash
+   # Let's create a 1GB swap partition
+   sudo fallocate -l 1G /swapfile
+   sudo chmod 600 /swapfile
+   sudo mkswap /swapfile
+   # Enable swap
+   sudo swapon /swapfile # you should now be able to see the swap size on htop.
+   # make changes permanent after reboot
+   sudo cp /etc/fstab /etc/fstab.bak
+   echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab
+   ```
+
+   Please note using swap as if it was RAM comes with a large penalty in terms of data movement.
+
+4. Run your Flower experiments following the steps in the [Running FL with Flower](https://github.com/adap/flower/tree/main/examples/embedded-devices#running-fl-training-with-flower) section.
 
 ## Setting up a Jetson Xavier-NX
 
-> These steps have been validated for a Jetson Xavier-NX Dev Kit. An identical setup is needed for a Jetson Nano and Jetson TX2 once you get ssh access to them (i.e. jumping straight to point `4` below). For instructions on how to setup these devices please refer to the "getting started guides" for [Jetson Nano](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit#intro) and [Jetson TX2](https://developer.nvidia.com/embedded/dlc/l4t-28-2-jetson-developer-kit-user-guide-ga).
+> These steps have been validated for a Jetson Xavier-NX Dev Kit. An identical setup is needed for a Jetson Nano once you get ssh access to it (i.e. jumping straight to point `4` below). For instructions on how to setup these devices please refer to the "getting started guides" for [Jetson Nano](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit#intro).
+
+1. **Install JetPack 5.1.2 on your Jetson device**
 
-1. Download the Ubuntu 18.04 image from [NVIDIA-embedded](https://developer.nvidia.com/embedded/downloads), note that you'll need a NVIDIA developer account. This image comes with Docker pre-installed as well as PyTorch+Torchvision compiled with GPU support.
+   - Download the JetPack 5.1.2 image from [NVIDIA-embedded](https://developer.nvidia.com/embedded/jetpack-sdk-512), note that you might need an NVIDIA developer account. You can find the download link under the `SD Card Image Method` section on NVIDIA's site. This image comes with Docker pre-installed as well as PyTorch+Torchvision and TensorFlow compiled with GPU support.
 
-2. Extract the image (~14GB) and flash it onto the uSD card using Etcher (or equivalent).
+   - Extract the image (~18GB and named `sd-blob.img`) and flash it onto the uSD card using [balenaEtcher](https://www.balena.io/etcher/) (or equivalent).
 
-3. Follow [the instructions](https://developer.nvidia.com/embedded/learn/get-started-jetson-xavier-nx-devkit) to setup the device.
+2. **Follow [the instructions](https://developer.nvidia.com/embedded/learn/get-started-jetson-xavier-nx-devkit) to set up the device.** The first time you boot your Xavier-NX you should plug it into a display to complete the installation process. After that, a display is no longer needed for this example but you could still use it instead of connecting to your device over ssh.
 
-4. Installing Docker: Docker comes pre-installed with the Ubuntu image provided by NVIDIA. But for convinience we will create a new user group and add our user to it (with the idea of not having to use `sudo` for every command involving docker (e.g. `docker run`, `docker ps`, etc)). More details about what this entails can be found in the [Docker documentation](https://docs.docker.com/engine/install/linux-postinstall/). You can achieve this by doing:
+3. **Setup Docker**: Docker comes pre-installed with the Ubuntu image provided by NVIDIA. But for convenience, we will create a new user group and add our user to it (with the idea of not having to use `sudo` for every command involving docker (e.g. `docker run`, `docker ps`, etc)). More details about what this entails can be found in the [Docker documentation](https://docs.docker.com/engine/install/linux-postinstall/). You can achieve this by doing:
 
    ```bash
-   $ sudo usermod -aG docker $USER
+   sudo usermod -aG docker $USER
    # apply changes to current shell (or logout/reboot)
-   $ newgrp docker
+   newgrp docker
    ```
 
-5. The minimal installation to run this example only requires an additional package, `git`, in order to clone this repo. Install `git` by:
+4. **Update OS and install utilities.** Then, install some useful utilities:
 
    ```bash
-   $ sudo apt-get update && sudo apt-get install git -y
+   sudo apt update && sudo apt upgrade -y
+   # now reboot
+   sudo reboot
    ```
 
-6. (optional) additional packages:
+   Login again and (optional) install the following packages:
+
    <img align="right" style="padding-top: 40px; padding-left: 15px" width="575" height="380" src="_static/tmux_jtop_view.gif">
 
-   - [jtop](https://github.com/rbonghi/jetson_stats),  to monitor CPU/GPU utilization, power consumption and, many more.
+   - [jtop](https://github.com/rbonghi/jetson_stats),  to monitor CPU/GPU utilization, power consumption and, many more. You can read more about it in [this blog post](https://jetsonhacks.com/2023/02/07/jtop-the-ultimate-tool-for-monitoring-nvidia-jetson-devices/).
 
      ```bash
      # First we need to install pip3
-     $ sudo apt-get install python3-pip -y
-     # updated pip3
-     $ sudo pip3 install -U pip
+     sudo apt install python3-pip -y
      # finally, install jtop
-     $ sudo -H pip3 install -U jetson-stats
+     sudo pip3 install -U jetson-stats
+     # now reboot (or run `sudo systemctl restart jtop.service` and login again)
+     sudo reboot
      ```
 
-   - [TMUX](https://github.com/tmux/tmux/wiki), a terminal multiplexer.
+     Now you have installed `jtop`, just launch it by running the `jtop` command on your terminal. An interactive panel similar to the one shown on the right will show up. `jtop` allows you to monitor and control many features of your Jetson device. Read more in the [jtop documentation](https://rnext.it/jetson_stats/jtop/jtop.html)
+
+   - [TMUX](https://github.com/tmux/tmux/wiki), a terminal multiplexer. As its name suggests, it allows you to device a single terminal window into multiple panels. In this way, you could (for example) use one panel to show your terminal and another to show `jtop`. That's precisely what the visualization on the right shows.
 
      ```bash
      # install tmux
-     $ sudo apt-get install tmux -y
+     sudo apt install tmux -y
      # add mouse support
-     $ echo set -g mouse on > ~/.tmux.conf
+     echo set -g mouse on > ~/.tmux.conf
      ```
 
-7. Power modes: The Jetson devices can operate at different power modes, each making use of more or less CPU cores clocked at different freqencies. The right power mode might very much depend on the application and scenario. When power consumption is not a limiting factor, we could use the highest 15W mode using all 6 CPU cores. On the other hand, if the devices are battery-powered we might want to make use of a low power mode using 10W and 2 CPU cores. All the details regarding the different power modes of a Jetson Xavier-NX can be found [here](https://docs.nvidia.com/jetson/l4t/index.html#page/Tegra%2520Linux%2520Driver%2520Package%2520Development%2520Guide%2Fpower_management_jetson_xavier.html%23wwpID0E0NO0HA). For this demo we'll be setting the device to the high performance mode:
+5. **Power modes**. The Jetson devices can operate at different power modes, each making use of more or less CPU cores clocked at different frequencies. The right power mode might very much depend on the application and scenario. When power consumption is not a limiting factor, we could use the highest 15W mode using all 6 CPU cores. On the other hand, if the devices are battery-powered we might want to make use of a low-power mode using 10W and 2 CPU cores. All the details regarding the different power modes of a Jetson Xavier-NX can be found [here](https://docs.nvidia.com/jetson/l4t/index.html#page/Tegra%2520Linux%2520Driver%2520Package%2520Development%2520Guide%2Fpower_management_jetson_xavier.html%23wwpID0E0NO0HA). For this demo, we'll be setting the device to high-performance mode:
 
    ```bash
-   $ sudo /usr/sbin/nvpmodel -m 2 # 15W with 6cpus @ 1.4GHz
+   sudo /usr/sbin/nvpmodel -m 2 # 15W with 6cpus @ 1.4GHz
    ```
 
-## Setting up a Raspberry Pi (3B+ or 4B)
-
-1. Install Ubuntu server 20.04 LTS 64-bit for Rapsberry Pi. You can do this by using one of the images provided [by Ubuntu](https://ubuntu.com/download/raspberry-pi) and then use Etcher. Alternativelly, astep-by-step installation guide, showing how to download and flash the image onto a uSD card and, go throught the first boot process, can be found [here](https://ubuntu.com/tutorials/how-to-install-ubuntu-on-your-raspberry-pi#1-overview). Please note that the first time you boot your RPi it will automatically update the system (which will lock `sudo` and prevent running the commands below for a few minutes)
+   Jetson Stats (that you launch via `jtop`) also allows you to see and set the power mode on your device. Navigate to the `CTRL` panel and click on one of the `NVM modes` available.
 
-2. Install docker (+ post-installation steps as in [Docker Docs](https://docs.docker.com/engine/install/linux-postinstall/)):
+6. **Build base client image**. Before running a Flower client, we need to install `Flower` and other ML dependencies (i.e. Pytorch or Tensorflow). Instead of installing this manually via `pip3 install ...`, let's use the pre-built Docker images provided by NVIDIA. In this way, we can be confident that the ML infrastructure is optimized for these devices. Build your Flower client image with:
 
    ```bash
-   # make sure your OS is up-to-date
-   $ sudo apt-get update
+   # On your Jetson's terminal run
+   ./build_jetson_flower_client.sh --pytorch # or --tensorflow
+   # Bear in mind this might take a few minutes since the base images need to be donwloaded (~7GB) and decompressed.
+   # To the above script pass the additional flag `--no-cache` to re-build the image.
+   ```
 
-   # get the installation script
-   $ curl -fsSL https://get.docker.com -o get-docker.sh
+   Once your script is finished, verify your `flower_client` Docker image is present. If you type `docker images` you'll see something like the following:
 
-   # install docker
-   $ sudo sh get-docker.sh
+   ```bash
+   REPOSITORY      TAG       IMAGE ID       CREATED          SIZE
+   flower_client   latest    87e935a8ee37   18 seconds ago   12.6GB
+   ```
 
-   # add your user to the docker group
-   $ sudo usermod -aG docker $USER
+7. **Access your client image**. Before launching the Flower client, we need to run the image we just created. To keep things simpler, let's run the image in interactive mode (`-it`), mount the entire repository you cloned inside the `/client` directory of your container (`` -v `pwd`:/client ``), and use the NVIDIA runtime so we can access the GPU `--runtime nvidia`:
 
-   # apply changes to current shell (or logout/reboot)
-   $ newgrp docker
+   ```bash
+   # first ensure you are in the `embedded-devices` directory. If you are not, use the `cd` command to navigate to it
+
+   # run the client container (this won't launch your Flower client, it will just "take you inside docker". The client can be run following the steps in the next section of the readme)
+   docker run -it --rm --runtime nvidia -v `pwd`:/client flower_client
+   # this will take you to a shell that looks something like this:
+   root@6e6ce826b8bb:/client# <here you can run python commands or any command as usual>
    ```
 
-. (optional) additional packages: you could install `TMUX` (see point `6` above) and `htop` as a replacement for `jtop` (which is only available for Jetson devices). Htop can be installed via: `sudo apt-get install htop -y`.
+8. **Run your FL experiments with Flower**. Follow the steps in the section below.
 
-## Running FL training with Flower
+## Running Embedded FL with Flower
 
-For this demo we'll be using [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html), a popular dataset for image classification comprised of 10 classes (e.g. car, bird, airplane) and a total of 60K `32x32` RGB images. The training set contains 50K images. The server will automatically download the dataset should it not be found in `./data`. To keep the client side simple, the datasets will be downloaded when building the docker image. This will happen as the first stage in both `run_pi.sh` and `run_jetson.sh`.
+For this demo, we'll be using [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html), a popular dataset for image classification comprised of 10 classes (e.g. car, bird, airplane) and a total of 60K `32x32` RGB images. The training set contains 50K images. The server will automatically download the dataset should it not be found in `./data`. The clients do the same. The dataset is by default split into 50 partitions (each to be assigned to a different client). This can be controlled with the `NUM_CLIENTS` global variable in the client scripts. In this example, each device will play the role of a specific user (specified via `--cid` -- we'll show this later) and therefore only do local training with that portion of the data. For CIFAR-10, clients will be training a MobileNet-v2/3 model.
 
-> If you'd like to make use of your own dataset you could [mount it](https://docs.docker.com/storage/volumes/) to the client docker container when calling `docker run`. We leave this an other more advanced topics for a future example.
+You can run this example using MNIST and a smaller CNN model by passing flag `--mnist`. This is useful if you are using devices with a very limited amount of memory (e.g. RaspberryPi Zero) or if you want the training taking place on the embedded devices to be much faster (specially if these are CPU-only). The partitioning of the dataset is done in the same way.
 
-### Server
+### Start your Flower Server
 
-Launch the server and define the model you'd like to train. The current code (see `utils.py`) provides two models for CIFAR-10: a small CNN (more suitable for Raspberry Pi) and, a ResNet18, which will run well on the gpu. Each model can be specified using the `--model` flag with options `Net` or `ResNet18`. Launch a FL training setup with one client and doing three rounds as:
+On the machine of your choice, launch the server:
 
 ```bash
-# launch your server. It will be waiting until one client connects
-$ python server.py --server_address <YOUR_SERVER_IP:PORT> --rounds 3 --min_num_clients 1 --min_sample_size 1 --model ResNet18
+# Launch your server.
+# Will wait for at least 2 clients to be connected, then will train for 3 FL rounds
+# The command below will sample all clients connected (since sample_fraction=1.0)
+python server.py --rounds 3 --min_num_clients 2 --sample_fraction 1.0 # append `--mnist` if you want to use that dataset/model setting
 ```
 
-### Clients
+> If you are on macOS with Apple Silicon (i.e. M1, M2 chips), you might encounter a `grpcio`-related issue when launching your server. If you are in a conda environment you can solve this easily by doing: `pip uninstall grpcio` and then `conda install grpcio`.
 
-Asuming you have cloned this repo onto the device/s, then execute the appropiate script to run the docker image, connect with the server and proceed with the training. Note that you can use both a Jetson and a RPi simultaneously, just make sure you modify the script above when launching the server so it waits until 2 clients are online.
+### Start the Flower Clients
 
-#### For Jetson
+It's time to launch your clients! Ensure you have followed the setup stages outline above for the devices at your disposal.
 
-```bash
-$ ./run_jetson.sh --server_address=<SERVER_ADDRESS> --cid=0 --model=ResNet18
-```
-
-#### For Raspberry Pi
+The first time you run this, the dataset will be downloaded. From the commands below, replace `<FRAMEWORK>` with either `pytorch` or `tf` to run the corresponding client Python file. In a FL setting, each client has its unique dataset. In this example you can simulate this by manually assigning an ID to a client (`cid`) which should be an integer `[0, NUM_CLIENTS-1]`, where `NUM_CLIENTS` is the total number of partitions or clients that could participate at any point. This is defined at the top of the client files and defaults to `50`. You can change this value to make each partition larger or smaller.
 
-Depending on the model of RapsberryPi you have, running the smaller `Net` model might be the only option due to the higher RAM budget needed for ResNet18. It should be fine for a RaspberryPi 4 with 4GB of RAM to run a RestNet18 (with an appropiate batch size) but bear in mind that each batch might take several second to complete. The following would run the smaller `Net` model:
+Launch your Flower clients as follows. Remember that if you are using a Jetson device, you need first to run your Docker container (see tha last steps for the Jetson setup). If you are using Raspberry Pi Zero devices, it is normal if starting the clients take a few seconds.
 
 ```bash
-# note that pulling the base image, extracting the content might take a while (specially on a RPi 3) the first time you run this.
-$ ./run_pi.sh --server_address=<SERVER_ADDRESS> --cid=0 --model=Net
+# Run the default example (CIFAR-10)
+python3 client_<FRAMEWORK>.py --cid=<CLIENT_ID> --server_address=<SERVER_ADDRESS>
+
+# Use MNIST (and a smaller model) if your devices require a more lightweight workload
+python3 client_<FRAMEWORK>.py --cid=<CLIENT_ID> --server_address=<SERVER_ADDRESS> --mnist
 ```
+
+Repeat the above for as many devices as you have. Pass a different `CLIENT_ID` to each device. You can naturally run this example using different types of devices (e.g. RPi, RPi Zero, Jetson) at the same time as long as they are training the same model. If you want to start more clients than the number of embedded devices you currently have access to, you can launch clients in your laptop: simply open a new terminal and run one of the `python3 client_<FRAMEWORK>.py ...` commands above.
diff --git a/examples/embedded-devices/_static/diagram.png b/examples/embedded-devices/_static/diagram.png
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diff --git a/examples/embedded-devices/base_images/README.md b/examples/embedded-devices/base_images/README.md
deleted file mode 100644
index b24608a466f9..000000000000
--- a/examples/embedded-devices/base_images/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
-## Building the base Docker images
-
-We provide the base images used in the Dockerfile of the parent directory (i.e. `jafermarq/jetsonfederated_cpu` and `jafermarq/jetsonfederated_gpu`). To make the process of running the demo as seamsless as possible (i.e. without long Docker build times) we have pre-built these images and uploaded them to dockerhub. In that way, the Dockerfile in the parent directory only requires adding a couple of python scripts to the image. If you want to build these images by yourself, you can do so by running the `build.sh` script in each directory. Note that building these images might take around one hour, depending on your system's specs.
-
-These images target a `aarch64` machine (e.g. RPi) but you'd probably will be building these images on a `x86_64` machine. To achieve this you'll need `qemu`. You should enable this before building the images by doing:
-
-```bash
-$ docker run --rm --privileged multiarch/qemu-user-static --reset -p yes
-```
-
-More details can be found in the [`qemu-user-static`](https://github.com/multiarch/qemu-user-static) repository.
diff --git a/examples/embedded-devices/base_images/cpu/Dockerfile b/examples/embedded-devices/base_images/cpu/Dockerfile
deleted file mode 100644
index 2f68406405a1..000000000000
--- a/examples/embedded-devices/base_images/cpu/Dockerfile
+++ /dev/null
@@ -1,43 +0,0 @@
-# From an ubuntu18 image bult for Arm, we build from source Pytorch and Torchvision
-# Then we install Flower
-# We also install dependencies for a very exciting future project
-
-FROM arm64v8/ubuntu:18.04
-
-# basics
-RUN apt-get update
-RUN apt-get install libopenblas-dev libopenmpi-dev python3-pip cmake -y
-
-# update pip
-RUN python3 -m pip install --upgrade pip
-
-RUN pip3 install Cython numpy
-
-RUN mkdir /app
-WORKDIR /app
-
-## Installing Pytorch + Torchvision
-RUN mkdir build
-WORKDIR build
-RUN apt-get install git bzip2 -y
-RUN pip3 install scikit-build ninja
-
-# PyTorch
-RUN git clone https://github.com/pytorch/pytorch.git
-WORKDIR pytorch
-RUN git checkout v1.6.0 && git submodule update --init --recursive
-ENV USE_NCCL=0 USE_QNNPACK=0 USE_PYTORCH_QNNPACK=0
-RUN pip3 install -r requirements.txt
-RUN python3 setup.py install
-
-# torchvision
-WORKDIR /app/build
-RUN git clone https://github.com/pytorch/vision.git
-# checkout v0.7.0 (the one compatible with PyTorch 1.6)
-WORKDIR vision
-RUN git checkout v0.7.0 && git submodule update --recursive
-RUN apt-get install libavcodec-dev libavformat-dev libswscale-dev libjpeg8-dev zlib1g-dev -y
-RUN python3 setup.py install
-
-WORKDIR /app
-RUN echo "done!"
diff --git a/examples/embedded-devices/base_images/cpu/build.sh b/examples/embedded-devices/base_images/cpu/build.sh
deleted file mode 100755
index be44de10de62..000000000000
--- a/examples/embedded-devices/base_images/cpu/build.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-if [ -z "${CI}" ]; then
-    BUILDKIT=1
-else
-    BUILDKIT=0
-fi
-
-DOCKER_BUILDKIT=${BUILDKIT} docker build $@ . -t jetsonfederated_cpu:latest
diff --git a/examples/embedded-devices/base_images/gpu/Dockerfile b/examples/embedded-devices/base_images/gpu/Dockerfile
deleted file mode 100644
index 50c0b8a895fd..000000000000
--- a/examples/embedded-devices/base_images/gpu/Dockerfile
+++ /dev/null
@@ -1,10 +0,0 @@
-# Uses a pre-built image from nvidia, then we install Flower
-# We also install dependencies for a very exciting future project
-
-# check this repo to learn more about the image below: https://github.com/dusty-nv/jetson-containers
-FROM nvcr.io/nvidia/l4t-pytorch:r32.5.0-pth1.6-py3
-
-RUN mkdir /app
-WORKDIR /app
-
-RUN echo "done!"
diff --git a/examples/embedded-devices/base_images/gpu/build.sh b/examples/embedded-devices/base_images/gpu/build.sh
deleted file mode 100755
index 7b0e91d4ad0b..000000000000
--- a/examples/embedded-devices/base_images/gpu/build.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-if [ -z "${CI}" ]; then
-    BUILDKIT=1
-else
-    BUILDKIT=0
-fi
-
-DOCKER_BUILDKIT=${BUILDKIT} docker build $@ . -t jetsonfederated_gpu:latest
diff --git a/examples/embedded-devices/build_image.sh b/examples/embedded-devices/build_image.sh
deleted file mode 100755
index ad19dd3a0e23..000000000000
--- a/examples/embedded-devices/build_image.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-
-if [ -z "${CI}" ]; then
-    BUILDKIT=1
-else
-    BUILDKIT=0
-fi
-
-# TODO: should we do a `docker pull` here ? 
-
-DOCKER_BUILDKIT=${BUILDKIT} docker build $@ . -t flower_client:latest
-
diff --git a/examples/embedded-devices/build_jetson_flower_client.sh b/examples/embedded-devices/build_jetson_flower_client.sh
new file mode 100755
index 000000000000..32725a58f1f7
--- /dev/null
+++ b/examples/embedded-devices/build_jetson_flower_client.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+
+if [ -z "${CI}" ]; then
+    BUILDKIT=1
+else
+    BUILDKIT=0
+fi
+
+# This script build a docker image that's ready to run your flower client.
+# Depending on your choice of ML framework (TF or PyTorch), the appropiate
+# base image from NVIDIA will be pulled. This ensures you get the best
+# performance out of your Jetson device.
+
+BASE_PYTORCH=nvcr.io/nvidia/l4t-pytorch:r35.1.0-pth1.13-py3
+BASE_TF=nvcr.io/nvidia/l4t-tensorflow:r35.3.1-tf2.11-py3
+EXTRA=""
+
+while [[ $# -gt 0 ]]; do
+  case $1 in
+    -p|--pytorch)
+      BASE_IMAGE=$BASE_PYTORCH
+      shift
+      ;;
+    -t|--tensorflow)
+      BASE_IMAGE=$BASE_TF
+      shift
+      ;;
+    -r|--no-cache)
+      EXTRA="--no-cache"
+      shift
+      ;;
+    -*|--*)
+      echo "Unknown option $1 (pass either --pytorch or --tensorflow)"
+      exit 1
+      ;;
+  esac
+done
+
+DOCKER_BUILDKIT=${BUILDKIT} docker build $EXTRA \
+                                        --build-arg BASE_IMAGE=$BASE_IMAGE \
+                                        . \
+                                        -t flower_client:latest
diff --git a/examples/embedded-devices/client.py b/examples/embedded-devices/client.py
deleted file mode 100644
index c0b7d6989d7f..000000000000
--- a/examples/embedded-devices/client.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# 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 client example using PyTorch for CIFAR-10 image classification."""
-
-
-import argparse
-import timeit
-from collections import OrderedDict
-from importlib import import_module
-
-import flwr as fl
-import numpy as np
-import torch
-import torchvision
-from flwr.common import (
-    EvaluateIns,
-    EvaluateRes,
-    FitIns,
-    FitRes,
-    ParametersRes,
-    NDArrays,
-)
-
-import utils
-
-# pylint: disable=no-member
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-# pylint: enable=no-member
-
-
-def get_weights(model: torch.nn.ModuleList) -> fl.common.NDArrays:
-    """Get model weights as a list of NumPy ndarrays."""
-    return [val.cpu().numpy() for _, val in model.state_dict().items()]
-
-
-def set_weights(model: torch.nn.ModuleList, weights: fl.common.NDArrays) -> None:
-    """Set model weights from a list of NumPy ndarrays."""
-    state_dict = OrderedDict(
-        {
-            k: torch.tensor(np.atleast_1d(v))
-            for k, v in zip(model.state_dict().keys(), weights)
-        }
-    )
-    model.load_state_dict(state_dict, strict=True)
-
-
-class CifarClient(fl.client.Client):
-    """Flower client implementing CIFAR-10 image classification using PyTorch."""
-
-    def __init__(
-        self,
-        cid: str,
-        model: torch.nn.Module,
-        trainset: torchvision.datasets.CIFAR10,
-        testset: torchvision.datasets.CIFAR10,
-    ) -> None:
-        self.cid = cid
-        self.model = model
-        self.trainset = trainset
-        self.testset = testset
-
-    def get_parameters(self, config) -> ParametersRes:
-        print(f"Client {self.cid}: get_parameters")
-
-        weights: NDArrays = get_weights(self.model)
-        parameters = fl.common.ndarrays_to_parameters(weights)
-        return ParametersRes(parameters=parameters)
-
-    def _instantiate_model(self, model_str: str):
-        # will load utils.model_str
-        m = getattr(import_module("utils"), model_str)
-        # instantiate model
-        self.model = m()
-
-    def fit(self, ins: FitIns) -> FitRes:
-        print(f"Client {self.cid}: fit")
-
-        weights: NDArrays = fl.common.parameters_to_ndarrays(ins.parameters)
-        config = ins.config
-        fit_begin = timeit.default_timer()
-
-        # Get training config
-        epochs = int(config["epochs"])
-        batch_size = int(config["batch_size"])
-        pin_memory = bool(config["pin_memory"])
-        num_workers = int(config["num_workers"])
-
-        # Set model parameters
-        set_weights(self.model, weights)
-
-        if torch.cuda.is_available():
-            kwargs = {
-                "num_workers": num_workers,
-                "pin_memory": pin_memory,
-                "drop_last": True,
-            }
-        else:
-            kwargs = {"drop_last": True}
-
-        # Train model
-        trainloader = torch.utils.data.DataLoader(
-            self.trainset, batch_size=batch_size, shuffle=True, **kwargs
-        )
-        utils.train(self.model, trainloader, epochs=epochs, device=DEVICE)
-
-        # Return the refined weights and the number of examples used for training
-        weights_prime: NDArrays = get_weights(self.model)
-        params_prime = fl.common.ndarrays_to_parameters(weights_prime)
-        num_examples_train = len(self.trainset)
-        metrics = {"duration": timeit.default_timer() - fit_begin}
-        return FitRes(
-            parameters=params_prime, num_examples=num_examples_train, metrics=metrics
-        )
-
-    def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
-        print(f"Client {self.cid}: evaluate")
-
-        weights = fl.common.parameters_to_ndarrays(ins.parameters)
-
-        # Use provided weights to update the local model
-        set_weights(self.model, weights)
-
-        # Evaluate the updated model on the local dataset
-        testloader = torch.utils.data.DataLoader(
-            self.testset, batch_size=32, shuffle=False
-        )
-        loss, accuracy = utils.test(self.model, testloader, device=DEVICE)
-
-        # Return the number of evaluation examples and the evaluation result (loss)
-        metrics = {"accuracy": float(accuracy)}
-        return EvaluateRes(
-            loss=float(loss), num_examples=len(self.testset), metrics=metrics
-        )
-
-
-def main() -> None:
-    """Load data, create and start CifarClient."""
-    parser = argparse.ArgumentParser(description="Flower")
-    parser.add_argument(
-        "--server_address",
-        type=str,
-        required=True,
-        help=f"gRPC server address",
-    )
-    parser.add_argument(
-        "--cid", type=str, required=True, help="Client CID (no default)"
-    )
-    parser.add_argument(
-        "--log_host",
-        type=str,
-        help="Logserver address (no default)",
-    )
-    parser.add_argument(
-        "--data_dir",
-        type=str,
-        help="Directory where the dataset lives",
-    )
-    parser.add_argument(
-        "--model",
-        type=str,
-        default="ResNet18",
-        choices=["Net", "ResNet18"],
-        help="model to train",
-    )
-    args = parser.parse_args()
-
-    # Configure logger
-    fl.common.logger.configure(f"client_{args.cid}", host=args.log_host)
-
-    # model
-    model = utils.load_model(args.model)
-    model.to(DEVICE)
-    # load (local, on-device) dataset
-    trainset, testset = utils.load_cifar()
-
-    # Start client
-    client = CifarClient(args.cid, model, trainset, testset)
-    fl.client.start_client(server_address=args.server_address, client=client)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/embedded-devices/client_pytorch.py b/examples/embedded-devices/client_pytorch.py
new file mode 100644
index 000000000000..5d236c9e9389
--- /dev/null
+++ b/examples/embedded-devices/client_pytorch.py
@@ -0,0 +1,216 @@
+import argparse
+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, random_split
+from torchvision.datasets import CIFAR10, MNIST
+from torchvision.transforms import Compose, Normalize, ToTensor
+from torchvision.models import mobilenet_v3_small
+from tqdm import tqdm
+
+parser = argparse.ArgumentParser(description="Flower Embedded devices")
+parser.add_argument(
+    "--server_address",
+    type=str,
+    default="0.0.0.0:8080",
+    help=f"gRPC server address (deafault '0.0.0.0:8080')",
+)
+parser.add_argument(
+    "--cid",
+    type=int,
+    required=True,
+    help="Client id. Should be an integer between 0 and NUM_CLIENTS",
+)
+parser.add_argument(
+    "--mnist",
+    action="store_true",
+    help="If you use Raspberry Pi Zero clients (which just have 512MB or RAM) use MNIST",
+)
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+NUM_CLIENTS = 50
+
+# a config for mobilenetv2 that works for
+# small input sizes (i.e. 32x32 as in CIFAR)
+mb2_cfg = [
+    (1, 16, 1, 1),
+    (6, 24, 2, 1),
+    (6, 32, 3, 2),
+    (6, 64, 4, 2),
+    (6, 96, 3, 1),
+    (6, 160, 3, 2),
+    (6, 320, 1, 1),
+]
+
+
+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(1, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 4 * 4, 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 * 4 * 4)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return self.fc3(x)
+
+
+def train(net, trainloader, optimizer, epochs, device):
+    """Train the model on the training set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    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, device):
+    """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 prepare_dataset(use_mnist: bool):
+    """Get MNIST/CIFAR-10 and return client partitions and global testset."""
+    dataset = MNIST if use_mnist else CIFAR10
+    if use_mnist:
+        norm = Normalize((0.1307,), (0.3081,))
+    else:
+        norm = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    trf = Compose([ToTensor(), norm])
+    trainset = dataset("./data", train=True, download=True, transform=trf)
+    testset = dataset("./data", train=False, download=True, transform=trf)
+
+    print("Partitioning dataset (IID)...")
+
+    # Split trainset into `num_partitions` trainsets
+    num_images = len(trainset) // NUM_CLIENTS
+    partition_len = [num_images] * NUM_CLIENTS
+
+    trainsets = random_split(
+        trainset, partition_len, torch.Generator().manual_seed(2023)
+    )
+
+    val_ratio = 0.1
+
+    # Create dataloaders with train+val support
+    train_partitions = []
+    val_partitions = []
+    for trainset_ in trainsets:
+        num_total = len(trainset_)
+        num_val = int(val_ratio * num_total)
+        num_train = num_total - num_val
+
+        for_train, for_val = random_split(
+            trainset_, [num_train, num_val], torch.Generator().manual_seed(2023)
+        )
+
+        train_partitions.append(for_train)
+        val_partitions.append(for_val)
+
+    return train_partitions, val_partitions, testset
+
+
+# Flower client, adapted from Pytorch quickstart/simulation example
+class FlowerClient(fl.client.NumPyClient):
+    """A FlowerClient that trains a MobileNetV3 model for CIFAR-10 or a much smaller CNN
+    for MNIST."""
+
+    def __init__(self, trainset, valset, use_mnist):
+        self.trainset = trainset
+        self.valset = valset
+        # Instantiate model
+        if use_mnist:
+            self.model = Net()
+        else:
+            self.model = mobilenet_v3_small(num_classes=10)
+            # let's not reduce spatial resolution too early
+            self.model.features[0][0].stride = (1, 1)
+        # Determine device
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)  # send model to device
+
+    def set_parameters(self, params):
+        """Set model weights from a list of NumPy ndarrays."""
+        params_dict = zip(self.model.state_dict().keys(), params)
+        state_dict = OrderedDict(
+            {
+                k: torch.Tensor(v) if v.shape != torch.Size([]) else torch.Tensor([0])
+                for k, v in params_dict
+            }
+        )
+        self.model.load_state_dict(state_dict, strict=True)
+
+    def get_parameters(self, config):
+        return [val.cpu().numpy() for _, val in self.model.state_dict().items()]
+
+    def fit(self, parameters, config):
+        print("Client sampled for fit()")
+        self.set_parameters(parameters)
+        # Read hyperparameters from config set by the server
+        batch, epochs = config["batch_size"], config["epochs"]
+        # Construct dataloader
+        trainloader = DataLoader(self.trainset, batch_size=batch, shuffle=True)
+        # Define optimizer
+        optimizer = torch.optim.SGD(self.model.parameters(), lr=0.01, momentum=0.9)
+        # Train
+        train(self.model, trainloader, optimizer, epochs=epochs, device=self.device)
+        # Return local model and statistics
+        return self.get_parameters({}), len(trainloader.dataset), {}
+
+    def evaluate(self, parameters, config):
+        print("Client sampled for evaluate()")
+        self.set_parameters(parameters)
+        # Construct dataloader
+        valloader = DataLoader(self.valset, batch_size=64)
+        # Evaluate
+        loss, accuracy = test(self.model, valloader, device=self.device)
+        # Return statistics
+        return float(loss), len(valloader.dataset), {"accuracy": float(accuracy)}
+
+
+def main():
+    args = parser.parse_args()
+    print(args)
+
+    assert args.cid < NUM_CLIENTS
+
+    use_mnist = args.mnist
+    # Download CIFAR-10 dataset and partition it
+    trainsets, valsets, _ = prepare_dataset(use_mnist)
+
+    # Start Flower client setting its associated data partition
+    fl.client.start_numpy_client(
+        server_address=args.server_address,
+        client=FlowerClient(
+            trainset=trainsets[args.cid], valset=valsets[args.cid], use_mnist=use_mnist
+        ),
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/embedded-devices/client_tf.py b/examples/embedded-devices/client_tf.py
new file mode 100644
index 000000000000..3457af1c7a66
--- /dev/null
+++ b/examples/embedded-devices/client_tf.py
@@ -0,0 +1,133 @@
+import math
+import argparse
+import warnings
+
+import flwr as fl
+import tensorflow as tf
+from tensorflow import keras as keras
+
+parser = argparse.ArgumentParser(description="Flower Embedded devices")
+parser.add_argument(
+    "--server_address",
+    type=str,
+    default="0.0.0.0:8080",
+    help=f"gRPC server address (deafault '0.0.0.0:8080')",
+)
+parser.add_argument(
+    "--cid",
+    type=int,
+    required=True,
+    help="Client id. Should be an integer between 0 and NUM_CLIENTS",
+)
+parser.add_argument(
+    "--mnist",
+    action="store_true",
+    help="If you use Raspberry Pi Zero clients (which just have 512MB or RAM) use MNIST",
+)
+
+warnings.filterwarnings("ignore", category=UserWarning)
+NUM_CLIENTS = 50
+
+
+def prepare_dataset(use_mnist: bool):
+    """Download and partitions the CIFAR-10/MNIST dataset."""
+    if use_mnist:
+        (x_train, y_train), testset = tf.keras.datasets.mnist.load_data()
+    else:
+        (x_train, y_train), testset = tf.keras.datasets.cifar10.load_data()
+    partitions = []
+    # We keep all partitions equal-sized in this example
+    partition_size = math.floor(len(x_train) / NUM_CLIENTS)
+    for cid in range(NUM_CLIENTS):
+        # Split dataset into non-overlapping NUM_CLIENT partitions
+        idx_from, idx_to = int(cid) * partition_size, (int(cid) + 1) * partition_size
+
+        x_train_cid, y_train_cid = (
+            x_train[idx_from:idx_to] / 255.0,
+            y_train[idx_from:idx_to],
+        )
+
+        # now partition into train/validation
+        # Use 10% of the client's training data for validation
+        split_idx = math.floor(len(x_train_cid) * 0.9)
+
+        client_train = (x_train_cid[:split_idx], y_train_cid[:split_idx])
+        client_val = (x_train_cid[split_idx:], y_train_cid[split_idx:])
+        partitions.append((client_train, client_val))
+
+    return partitions, testset
+
+
+class FlowerClient(fl.client.NumPyClient):
+    """A FlowerClient that uses MobileNetV3 for CIFAR-10 or a much smaller CNN for
+    MNIST."""
+
+    def __init__(self, trainset, valset, use_mnist: bool):
+        self.x_train, self.y_train = trainset
+        self.x_val, self.y_val = valset
+        # Instantiate model
+        if use_mnist:
+            # small model for MNIST
+            self.model = model = keras.Sequential(
+                [
+                    keras.Input(shape=(28, 28, 1)),
+                    keras.layers.Conv2D(32, kernel_size=(5, 5), activation="relu"),
+                    keras.layers.MaxPooling2D(pool_size=(2, 2)),
+                    keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
+                    keras.layers.MaxPooling2D(pool_size=(2, 2)),
+                    keras.layers.Flatten(),
+                    keras.layers.Dropout(0.5),
+                    keras.layers.Dense(10, activation="softmax"),
+                ]
+            )
+        else:
+            # let's use a larger model for cifar
+            self.model = tf.keras.applications.MobileNetV3Small(
+                (32, 32, 3), classes=10, weights=None
+            )
+        self.model.compile(
+            "adam", "sparse_categorical_crossentropy", metrics=["accuracy"]
+        )
+
+    def get_parameters(self, config):
+        return self.model.get_weights()
+
+    def set_parameters(self, params):
+        self.model.set_weights(params)
+
+    def fit(self, parameters, config):
+        print("Client sampled for fit()")
+        self.set_parameters(parameters)
+        # Set hyperparameters from config sent by server/strategy
+        batch, epochs = config["batch_size"], config["epochs"]
+        # train
+        self.model.fit(self.x_train, self.y_train, epochs=epochs, batch_size=batch)
+        return self.get_parameters({}), len(self.x_train), {}
+
+    def evaluate(self, parameters, config):
+        print("Client sampled for evaluate()")
+        self.set_parameters(parameters)
+        loss, accuracy = self.model.evaluate(self.x_val, self.y_val)
+        return loss, len(self.x_val), {"accuracy": accuracy}
+
+
+def main():
+    args = parser.parse_args()
+    print(args)
+
+    assert args.cid < NUM_CLIENTS
+
+    use_mnist = args.mnist
+    # Download CIFAR-10 dataset and partition it
+    partitions, _ = prepare_dataset(use_mnist)
+    trainset, valset = partitions[args.cid]
+
+    # Start Flower client setting its associated data partition
+    fl.client.start_numpy_client(
+        server_address=args.server_address,
+        client=FlowerClient(trainset=trainset, valset=valset, use_mnist=use_mnist),
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/embedded-devices/requirements.txt b/examples/embedded-devices/requirements.txt
deleted file mode 100644
index cdb29230ffeb..000000000000
--- a/examples/embedded-devices/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-flwr==1.3.0
-numpy==1.24.2
-torch==2.0.0
-torchvision==0.15.1
diff --git a/examples/embedded-devices/requirements_pytorch.txt b/examples/embedded-devices/requirements_pytorch.txt
new file mode 100644
index 000000000000..797ca6db6244
--- /dev/null
+++ b/examples/embedded-devices/requirements_pytorch.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/embedded-devices/requirements_tf.txt b/examples/embedded-devices/requirements_tf.txt
new file mode 100644
index 000000000000..c7068d40b9c2
--- /dev/null
+++ b/examples/embedded-devices/requirements_tf.txt
@@ -0,0 +1,2 @@
+flwr>=1.0, <2.0
+tensorflow >=2.9.1, != 2.11.1
diff --git a/examples/embedded-devices/run_jetson.sh b/examples/embedded-devices/run_jetson.sh
deleted file mode 100755
index 4939506081aa..000000000000
--- a/examples/embedded-devices/run_jetson.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# 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.
-# ==============================================================================
-
-
-# run as: ./run_jetson.sh --server_address=<YOUR_SERVER_ADDRESS> --cid=0 --model=ResNet18
-
-echo "ARGS: ${@}"
-
-./build_image.sh --build-arg BASE_IMAGE_TYPE=gpu
-
-docker run --runtime nvidia --rm flower_client ${@}
diff --git a/examples/embedded-devices/run_pi.sh b/examples/embedded-devices/run_pi.sh
deleted file mode 100755
index 9a2dae23bf46..000000000000
--- a/examples/embedded-devices/run_pi.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# 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.
-# ==============================================================================
-
-
-# run as: ./run_pi.sh --server_address=<YOUR_SERVER_ADDRESS> --cid=0 --model=Net
-
-echo "ARGS: ${@}"
-
-./build_image.sh --build-arg BASE_IMAGE_TYPE=cpu
-
-docker run --rm flower_client ${@}
diff --git a/examples/embedded-devices/server.py b/examples/embedded-devices/server.py
index bdf12d6fe640..2a15f792297e 100644
--- a/examples/embedded-devices/server.py
+++ b/examples/embedded-devices/server.py
@@ -1,47 +1,22 @@
-# 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.
-# ==============================================================================
-"""Minimal example on how to start a simple Flower server."""
-
-
 import argparse
-from collections import OrderedDict
-from typing import Callable, Dict, Optional, Tuple
+from typing import List, Tuple
 
 import flwr as fl
-import numpy as np
-import torch
-import torchvision
-
-import utils
+from flwr.common import Metrics
 
-# pylint: disable=no-member
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-# pylint: enable=no-member
 
-parser = argparse.ArgumentParser(description="Flower")
+parser = argparse.ArgumentParser(description="Flower Embedded devices")
 parser.add_argument(
     "--server_address",
     type=str,
-    required=True,
-    help=f"gRPC server address",
+    default="0.0.0.0:8080",
+    help=f"gRPC server address (deafault '0.0.0.0:8080')",
 )
 parser.add_argument(
     "--rounds",
     type=int,
-    default=1,
-    help="Number of rounds of federated learning (default: 1)",
+    default=5,
+    help="Number of rounds of federated learning (default: 5)",
 )
 parser.add_argument(
     "--sample_fraction",
@@ -49,12 +24,6 @@
     default=1.0,
     help="Fraction of available clients used for fit/evaluate (default: 1.0)",
 )
-parser.add_argument(
-    "--min_sample_size",
-    type=int,
-    default=2,
-    help="Minimum number of clients used for fit/evaluate (default: 2)",
-)
 parser.add_argument(
     "--min_num_clients",
     type=int,
@@ -62,107 +31,54 @@
     help="Minimum number of available clients required for sampling (default: 2)",
 )
 parser.add_argument(
-    "--log_host",
-    type=str,
-    help="Logserver address (no default)",
-)
-parser.add_argument(
-    "--model",
-    type=str,
-    default="ResNet18",
-    choices=["Net", "ResNet18"],
-    help="model to train",
-)
-parser.add_argument(
-    "--batch_size",
-    type=int,
-    default=32,
-    help="training batch size",
-)
-parser.add_argument(
-    "--num_workers",
-    type=int,
-    default=4,
-    help="number of workers for dataset reading",
+    "--mnist",
+    action="store_true",
+    help="If you use Raspberry Pi Zero clients (which just have 512MB or RAM) use MNIST",
 )
-parser.add_argument("--pin_memory", action="store_true")
-args = parser.parse_args()
 
 
-def main() -> None:
-    """Start server and train five rounds."""
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    """Thist function averages teh `accuracy` metric sent by the clients in a `evaluate`
+    stage (i.e. clients received the global model and evaluate it on their local
+    validation sets)."""
+    # 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]
 
-    print(args)
+    # Aggregate and return custom metric (weighted average)
+    return {"accuracy": sum(accuracies) / sum(examples)}
+
+
+def fit_config(server_round: int):
+    """Return a configuration with static batch size and (local) epochs."""
+    config = {
+        "epochs": 3,  # Number of local epochs done by clients
+        "batch_size": 16,  # Batch size to use by clients during fit()
+    }
+    return config
 
-    assert (
-        args.min_sample_size <= args.min_num_clients
-    ), f"Num_clients shouldn't be lower than min_sample_size"
 
-    # Configure logger
-    fl.common.logger.configure("server", host=args.log_host)
+def main():
+    args = parser.parse_args()
 
-    # Load evaluation data
-    _, testset = utils.load_cifar(download=True)
+    print(args)
 
-    # Create client_manager, strategy, and server
-    client_manager = fl.server.SimpleClientManager()
+    # Define strategy
     strategy = fl.server.strategy.FedAvg(
         fraction_fit=args.sample_fraction,
-        min_fit_clients=args.min_sample_size,
-        min_available_clients=args.min_num_clients,
-        evaluate_fn=get_eval_fn(testset),
+        fraction_evaluate=args.sample_fraction,
+        min_fit_clients=args.min_num_clients,
         on_fit_config_fn=fit_config,
+        evaluate_metrics_aggregation_fn=weighted_average,
     )
-    server = fl.server.Server(client_manager=client_manager, strategy=strategy)
 
-    # Run server
+    # Start Flower server
     fl.server.start_server(
         server_address=args.server_address,
-        server=server,
-        config=fl.server.ServerConfig(num_rounds=args.rounds),
-    )
-
-
-def fit_config(server_round: int) -> Dict[str, fl.common.Scalar]:
-    """Return a configuration with static batch size and (local) epochs."""
-    config = {
-        "epoch_global": str(server_round),
-        "epochs": str(1),
-        "batch_size": str(args.batch_size),
-        "num_workers": str(args.num_workers),
-        "pin_memory": str(args.pin_memory),
-    }
-    return config
-
-
-def set_weights(model: torch.nn.ModuleList, weights: fl.common.NDArrays) -> None:
-    """Set model weights from a list of NumPy ndarrays."""
-    state_dict = OrderedDict(
-        {
-            k: torch.tensor(np.atleast_1d(v))
-            for k, v in zip(model.state_dict().keys(), weights)
-        }
+        config=fl.server.ServerConfig(num_rounds=3),
+        strategy=strategy,
     )
-    model.load_state_dict(state_dict, strict=True)
-
-
-def get_eval_fn(
-    testset: torchvision.datasets.CIFAR10,
-) -> Callable[[fl.common.NDArrays], Optional[Tuple[float, float]]]:
-    """Return an evaluation function for centralized evaluation."""
-
-    def evaluate(weights: fl.common.NDArrays) -> Optional[Tuple[float, float]]:
-        """Use the entire CIFAR-10 test set for evaluation."""
-
-        model = utils.load_model(args.model)
-        set_weights(model, weights)
-        model.to(DEVICE)
-
-        testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)
-        loss, accuracy = utils.test(model, testloader, device=DEVICE)
-        return loss, {"accuracy": accuracy}
-
-    return evaluate
 
 
 if __name__ == "__main__":
diff --git a/examples/embedded-devices/utils.py b/examples/embedded-devices/utils.py
deleted file mode 100644
index c0946a758e45..000000000000
--- a/examples/embedded-devices/utils.py
+++ /dev/null
@@ -1,175 +0,0 @@
-# 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.
-# ==============================================================================
-"""PyTorch CIFAR-10 image classification.
-
-The code is generally adapted from 'PyTorch: A 60 Minute Blitz'. Further
-explanations are given in the official PyTorch tutorial:
-
-https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
-"""
-
-
-# mypy: ignore-errors
-# pylint: disable=W0223
-
-
-from collections import OrderedDict
-from pathlib import Path
-from time import time
-from typing import Tuple
-
-import flwr as fl
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-from torch import Tensor
-from torchvision import datasets
-from torchvision.models import resnet18
-
-DATA_ROOT = Path("./data")
-
-
-# pylint: disable=unsubscriptable-object
-class Net(nn.Module):
-    """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)
-
-    # pylint: disable=arguments-differ,invalid-name
-    def forward(self, x: Tensor) -> Tensor:
-        """Compute forward pass."""
-        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))
-        x = self.fc3(x)
-        return x
-
-    def get_weights(self) -> fl.common.NDArrays:
-        """Get model weights as a list of NumPy ndarrays."""
-        return [val.cpu().numpy() for _, val in self.state_dict().items()]
-
-    def set_weights(self, weights: fl.common.NDArrays) -> None:
-        """Set model weights from a list of NumPy ndarrays."""
-        state_dict = OrderedDict(
-            {k: torch.tensor(v) for k, v in zip(self.state_dict().keys(), weights)}
-        )
-        self.load_state_dict(state_dict, strict=True)
-
-
-def ResNet18():
-    """Returns a ResNet18 model from TorchVision adapted for CIFAR-10."""
-
-    model = resnet18(num_classes=10)
-
-    # replace w/ smaller input layer
-    model.conv1 = torch.nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
-    nn.init.kaiming_normal_(model.conv1.weight, mode="fan_out", nonlinearity="relu")
-    # no need for pooling if training for CIFAR-10
-    model.maxpool = torch.nn.Identity()
-
-    return model
-
-
-def load_model(model_name: str) -> nn.Module:
-    if model_name == "Net":
-        return Net()
-    elif model_name == "ResNet18":
-        return ResNet18()
-    else:
-        raise NotImplementedError(f"model {model_name} is not implemented")
-
-
-# pylint: disable=unused-argument
-def load_cifar(download=False) -> Tuple[datasets.CIFAR10, datasets.CIFAR10]:
-    """Load CIFAR-10 (training and test set)."""
-    transform = transforms.Compose(
-        [
-            transforms.ToTensor(),
-            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-        ]
-    )
-    trainset = datasets.CIFAR10(
-        root=DATA_ROOT / "cifar-10", train=True, download=download, transform=transform
-    )
-    testset = datasets.CIFAR10(
-        root=DATA_ROOT / "cifar-10", train=False, download=download, transform=transform
-    )
-    return trainset, testset
-
-
-def train(
-    net: Net,
-    trainloader: torch.utils.data.DataLoader,
-    epochs: int,
-    device: torch.device,  # pylint: disable=no-member
-) -> None:
-    """Train the network."""
-    # Define loss and optimizer
-    criterion = nn.CrossEntropyLoss()
-    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
-
-    print(f"Training {epochs} epoch(s) w/ {len(trainloader)} batches each")
-    t = time()
-    # Train the network
-    for epoch in range(epochs):  # loop over the dataset multiple times
-        running_loss = 0.0
-        for i, data in enumerate(trainloader, 0):
-            images, labels = data[0].to(device), data[1].to(device)
-
-            # zero the parameter gradients
-            optimizer.zero_grad()
-
-            # forward + backward + optimize
-            outputs = net(images)
-            loss = criterion(outputs, labels)
-            loss.backward()
-            optimizer.step()
-
-            # print statistics
-            running_loss += loss.item()
-            if i % 2000 == 1999:  # print every 2000 mini-batches
-                print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1, running_loss / 2000))
-                running_loss = 0.0
-
-    print(f"Epoch took: {time() - t:.2f} seconds")
-
-
-def test(
-    net: Net,
-    testloader: torch.utils.data.DataLoader,
-    device: torch.device,  # pylint: disable=no-member
-) -> Tuple[float, float]:
-    """Validate the network on the entire test set."""
-    criterion = nn.CrossEntropyLoss()
-    correct, loss = 0, 0.0
-    with torch.no_grad():
-        for data in testloader:
-            images, labels = data[0].to(device), data[1].to(device)
-            outputs = net(images)
-            loss += criterion(outputs, labels).item()
-            _, predicted = torch.max(outputs.data, 1)  # pylint: disable=no-member
-            correct += (predicted == labels).sum().item()
-    accuracy = correct / len(testloader.dataset)
-    return loss, accuracy

From e1ba808462eb1e752766c22e9441b59006e2570d Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 6 Oct 2023 15:10:33 +0100
Subject: [PATCH 31/60] Fix readme urls (#2446)

---
 doc/source/ref-changelog.md      | 2 +-
 examples/secaggplus-mt/README.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 86993542ea4d..d42f782bb11f 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -22,7 +22,7 @@
 
 - **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331))
 
-- **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))
+- **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))
 
   Flower received many improvements under the hood, too many to list here.
 
diff --git a/examples/secaggplus-mt/README.md b/examples/secaggplus-mt/README.md
index 164174509d4c..0b3b4db3942e 100644
--- a/examples/secaggplus-mt/README.md
+++ b/examples/secaggplus-mt/README.md
@@ -1,6 +1,6 @@
 # Secure Aggregation with Driver API
 
-This example contains highly experimental code. Please consult the regular PyTorch code examples ([quickstart](https://github.com/adap/flower/tree/main/examples/quickstart_pytorch), [advanced](https://github.com/adap/flower/tree/main/examples/advanced_pytorch)) to learn how to use Flower with PyTorch.
+This example contains highly experimental code. Please consult the regular PyTorch code examples ([quickstart](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch), [advanced](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)) to learn how to use Flower with PyTorch.
 
 ## Installing Dependencies
 

From e7977b58868dab6f189acdfe19b6ded343c76fd7 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Sat, 7 Oct 2023 12:45:00 +0100
Subject: [PATCH 32/60] Change `workload_id` type to `uint64` (#2413)

---
 src/proto/flwr/proto/driver.proto               |  4 ++--
 src/proto/flwr/proto/task.proto                 |  4 ++--
 src/py/flwr/client/grpc_client/connection.py    |  2 +-
 .../client/message_handler/message_handler.py   |  2 +-
 .../message_handler/message_handler_test.py     |  8 ++++----
 .../flwr/client/message_handler/task_handler.py |  2 +-
 .../client/message_handler/task_handler_test.py |  2 +-
 src/py/flwr/driver/app_test.py                  |  2 +-
 src/py/flwr/driver/driver_client_proxy.py       |  2 +-
 src/py/flwr/driver/driver_client_proxy_test.py  | 16 ++++++++--------
 src/py/flwr/proto/driver_pb2.py                 |  2 +-
 src/py/flwr/proto/driver_pb2.pyi                |  8 ++++----
 src/py/flwr/proto/task_pb2.py                   |  2 +-
 src/py/flwr/proto/task_pb2.pyi                  |  8 ++++----
 .../message_handler/message_handler_test.py     |  2 +-
 src/py/flwr/server/state/in_memory_state.py     | 13 ++++++-------
 src/py/flwr/server/state/sqlite_state.py        | 17 ++++++++---------
 src/py/flwr/server/state/sqlite_state_test.py   |  2 +-
 src/py/flwr/server/state/state.py               |  4 ++--
 src/py/flwr/server/state/state_test.py          |  8 ++++----
 src/py/flwr/server/utils/validator_test.py      |  4 ++--
 21 files changed, 56 insertions(+), 58 deletions(-)

diff --git a/src/proto/flwr/proto/driver.proto b/src/proto/flwr/proto/driver.proto
index 1cfb77135d5a..1caaad88a0da 100644
--- a/src/proto/flwr/proto/driver.proto
+++ b/src/proto/flwr/proto/driver.proto
@@ -36,10 +36,10 @@ service Driver {
 
 // CreateWorkload
 message CreateWorkloadRequest {}
-message CreateWorkloadResponse { string workload_id = 1; }
+message CreateWorkloadResponse { uint64 workload_id = 1; }
 
 // GetNodes messages
-message GetNodesRequest { string workload_id = 1; }
+message GetNodesRequest { uint64 workload_id = 1; }
 message GetNodesResponse { repeated Node nodes = 1; }
 
 // PushTaskIns messages
diff --git a/src/proto/flwr/proto/task.proto b/src/proto/flwr/proto/task.proto
index 29e07641bb1c..d87fb39c2637 100644
--- a/src/proto/flwr/proto/task.proto
+++ b/src/proto/flwr/proto/task.proto
@@ -36,14 +36,14 @@ message Task {
 message TaskIns {
   string task_id = 1;
   string group_id = 2;
-  string workload_id = 3;
+  uint64 workload_id = 3;
   Task task = 4;
 }
 
 message TaskRes {
   string task_id = 1;
   string group_id = 2;
-  string workload_id = 3;
+  uint64 workload_id = 3;
   Task task = 4;
 }
 
diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index eda869d3a326..cc64ec9a268a 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -117,7 +117,7 @@ def receive() -> TaskIns:
         return TaskIns(
             task_id=str(uuid.uuid4()),
             group_id="",
-            workload_id="",
+            workload_id=0,
             task=Task(
                 producer=Node(node_id=0, anonymous=True),
                 consumer=Node(node_id=0, anonymous=True),
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index 9b26a9bd5ca0..f50923450f62 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -76,7 +76,7 @@ def handle(client_fn: ClientFn, task_ins: TaskIns) -> Tuple[TaskRes, int, bool]:
             task_res = TaskRes(
                 task_id="",
                 group_id="",
-                workload_id="",
+                workload_id=0,
                 task=Task(
                     ancestry=[],
                     sa=SecureAggregation(named_values=serde.named_values_to_proto(res)),
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 31cbb00edf63..1fc2269ad75d 100644
--- a/src/py/flwr/client/message_handler/message_handler_test.py
+++ b/src/py/flwr/client/message_handler/message_handler_test.py
@@ -120,7 +120,7 @@ def test_client_without_get_properties() -> None:
     task_ins: TaskIns = TaskIns(
         task_id=str(uuid.uuid4()),
         group_id="",
-        workload_id="",
+        workload_id=0,
         task=Task(
             producer=Node(node_id=0, anonymous=True),
             consumer=Node(node_id=0, anonymous=True),
@@ -146,7 +146,7 @@ def test_client_without_get_properties() -> None:
         TaskRes(
             task_id=str(uuid.uuid4()),
             group_id="",
-            workload_id="",
+            workload_id=0,
         )
     )
     # pylint: disable=no-member
@@ -183,7 +183,7 @@ def test_client_with_get_properties() -> None:
     task_ins = TaskIns(
         task_id=str(uuid.uuid4()),
         group_id="",
-        workload_id="",
+        workload_id=0,
         task=Task(
             producer=Node(node_id=0, anonymous=True),
             consumer=Node(node_id=0, anonymous=True),
@@ -209,7 +209,7 @@ def test_client_with_get_properties() -> None:
         TaskRes(
             task_id=str(uuid.uuid4()),
             group_id="",
-            workload_id="",
+            workload_id=0,
         )
     )
     # pylint: disable=no-member
diff --git a/src/py/flwr/client/message_handler/task_handler.py b/src/py/flwr/client/message_handler/task_handler.py
index 03688c52ac8f..b48c7433c1da 100644
--- a/src/py/flwr/client/message_handler/task_handler.py
+++ b/src/py/flwr/client/message_handler/task_handler.py
@@ -129,7 +129,7 @@ def wrap_client_message_in_task_res(client_message: ClientMessage) -> TaskRes:
     return TaskRes(
         task_id="",
         group_id="",
-        workload_id="",
+        workload_id=0,
         task=Task(ancestry=[], legacy_client_message=client_message),
     )
 
diff --git a/src/py/flwr/client/message_handler/task_handler_test.py b/src/py/flwr/client/message_handler/task_handler_test.py
index 347b9ad32c4b..e1b7fac69d24 100644
--- a/src/py/flwr/client/message_handler/task_handler_test.py
+++ b/src/py/flwr/client/message_handler/task_handler_test.py
@@ -92,7 +92,7 @@ def test_validate_task_res() -> None:
     assert not validate_task_res(task_res)
 
     task_res.Clear()
-    task_res.workload_id = "123"
+    task_res.workload_id = 61016
     assert not validate_task_res(task_res)
 
     task_res.Clear()
diff --git a/src/py/flwr/driver/app_test.py b/src/py/flwr/driver/app_test.py
index 792bd84b6106..4fcd924f8432 100644
--- a/src/py/flwr/driver/app_test.py
+++ b/src/py/flwr/driver/app_test.py
@@ -43,7 +43,7 @@ def test_simple_client_manager_update(self) -> None:
         ]
         driver = MagicMock()
         driver.stub = "driver stub"
-        driver.create_workload.return_value = CreateWorkloadResponse(workload_id="1")
+        driver.create_workload.return_value = CreateWorkloadResponse(workload_id=1)
         driver.get_nodes.return_value = GetNodesResponse(nodes=expected_nodes)
         client_manager = SimpleClientManager()
         lock = threading.Lock()
diff --git a/src/py/flwr/driver/driver_client_proxy.py b/src/py/flwr/driver/driver_client_proxy.py
index cd5d36cafdd7..deb472458a15 100644
--- a/src/py/flwr/driver/driver_client_proxy.py
+++ b/src/py/flwr/driver/driver_client_proxy.py
@@ -31,7 +31,7 @@
 class DriverClientProxy(ClientProxy):
     """Flower client proxy which delegates work using the Driver API."""
 
-    def __init__(self, node_id: int, driver: Driver, anonymous: bool, workload_id: str):
+    def __init__(self, node_id: int, driver: Driver, anonymous: bool, workload_id: int):
         super().__init__(str(node_id))
         self.node_id = node_id
         self.driver = driver
diff --git a/src/py/flwr/driver/driver_client_proxy_test.py b/src/py/flwr/driver/driver_client_proxy_test.py
index fa2a29e88687..f413b8d8d99d 100644
--- a/src/py/flwr/driver/driver_client_proxy_test.py
+++ b/src/py/flwr/driver/driver_client_proxy_test.py
@@ -52,7 +52,7 @@ def test_get_properties(self) -> None:
                 task_pb2.TaskRes(
                     task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
                     group_id="",
-                    workload_id="",
+                    workload_id=0,
                     task=task_pb2.Task(
                         legacy_client_message=ClientMessage(
                             get_properties_res=ClientMessage.GetPropertiesRes(
@@ -64,7 +64,7 @@ def test_get_properties(self) -> None:
             ]
         )
         client = DriverClientProxy(
-            node_id=1, driver=self.driver, anonymous=True, workload_id=""
+            node_id=1, driver=self.driver, anonymous=True, workload_id=0
         )
         request_properties: Config = {"tensor_type": "str"}
         ins: flwr.common.GetPropertiesIns = flwr.common.GetPropertiesIns(
@@ -88,7 +88,7 @@ def test_get_parameters(self) -> None:
                 task_pb2.TaskRes(
                     task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
                     group_id="",
-                    workload_id="",
+                    workload_id=0,
                     task=task_pb2.Task(
                         legacy_client_message=ClientMessage(
                             get_parameters_res=ClientMessage.GetParametersRes(
@@ -100,7 +100,7 @@ def test_get_parameters(self) -> None:
             ]
         )
         client = DriverClientProxy(
-            node_id=1, driver=self.driver, anonymous=True, workload_id=""
+            node_id=1, driver=self.driver, anonymous=True, workload_id=0
         )
         get_parameters_ins = GetParametersIns(config={})
 
@@ -123,7 +123,7 @@ def test_fit(self) -> None:
                 task_pb2.TaskRes(
                     task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
                     group_id="",
-                    workload_id="",
+                    workload_id=0,
                     task=task_pb2.Task(
                         legacy_client_message=ClientMessage(
                             fit_res=ClientMessage.FitRes(
@@ -136,7 +136,7 @@ def test_fit(self) -> None:
             ]
         )
         client = DriverClientProxy(
-            node_id=1, driver=self.driver, anonymous=True, workload_id=""
+            node_id=1, driver=self.driver, anonymous=True, workload_id=0
         )
         parameters = flwr.common.ndarrays_to_parameters([np.ones((2, 2))])
         ins: flwr.common.FitIns = flwr.common.FitIns(parameters, {})
@@ -160,7 +160,7 @@ def test_evaluate(self) -> None:
                 task_pb2.TaskRes(
                     task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
                     group_id="",
-                    workload_id="",
+                    workload_id=0,
                     task=task_pb2.Task(
                         legacy_client_message=ClientMessage(
                             evaluate_res=ClientMessage.EvaluateRes(
@@ -172,7 +172,7 @@ def test_evaluate(self) -> None:
             ]
         )
         client = DriverClientProxy(
-            node_id=1, driver=self.driver, anonymous=True, workload_id=""
+            node_id=1, driver=self.driver, anonymous=True, workload_id=0
         )
         parameters = flwr.common.Parameters(tensors=[], tensor_type="np")
         evaluate_ins: flwr.common.EvaluateIns = flwr.common.EvaluateIns(parameters, {})
diff --git a/src/py/flwr/proto/driver_pb2.py b/src/py/flwr/proto/driver_pb2.py
index c18d9c593c28..6ac066d7eab3 100644
--- a/src/py/flwr/proto/driver_pb2.py
+++ b/src/py/flwr/proto/driver_pb2.py
@@ -16,7 +16,7 @@
 from flwr.proto import task_pb2 as flwr_dot_proto_dot_task__pb2
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x17\x66lwr/proto/driver.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x15\x66lwr/proto/task.proto\"\x17\n\x15\x43reateWorkloadRequest\"-\n\x16\x43reateWorkloadResponse\x12\x13\n\x0bworkload_id\x18\x01 \x01(\t\"&\n\x0fGetNodesRequest\x12\x13\n\x0bworkload_id\x18\x01 \x01(\t\"3\n\x10GetNodesResponse\x12\x1f\n\x05nodes\x18\x01 \x03(\x0b\x32\x10.flwr.proto.Node\"@\n\x12PushTaskInsRequest\x12*\n\rtask_ins_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskIns\"\'\n\x13PushTaskInsResponse\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"F\n\x12PullTaskResRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"A\n\x13PullTaskResResponse\x12*\n\rtask_res_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskRes2\xd0\x02\n\x06\x44river\x12Y\n\x0e\x43reateWorkload\x12!.flwr.proto.CreateWorkloadRequest\x1a\".flwr.proto.CreateWorkloadResponse\"\x00\x12G\n\x08GetNodes\x12\x1b.flwr.proto.GetNodesRequest\x1a\x1c.flwr.proto.GetNodesResponse\"\x00\x12P\n\x0bPushTaskIns\x12\x1e.flwr.proto.PushTaskInsRequest\x1a\x1f.flwr.proto.PushTaskInsResponse\"\x00\x12P\n\x0bPullTaskRes\x12\x1e.flwr.proto.PullTaskResRequest\x1a\x1f.flwr.proto.PullTaskResResponse\"\x00\x62\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x17\x66lwr/proto/driver.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x15\x66lwr/proto/task.proto\"\x17\n\x15\x43reateWorkloadRequest\"-\n\x16\x43reateWorkloadResponse\x12\x13\n\x0bworkload_id\x18\x01 \x01(\x04\"&\n\x0fGetNodesRequest\x12\x13\n\x0bworkload_id\x18\x01 \x01(\x04\"3\n\x10GetNodesResponse\x12\x1f\n\x05nodes\x18\x01 \x03(\x0b\x32\x10.flwr.proto.Node\"@\n\x12PushTaskInsRequest\x12*\n\rtask_ins_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskIns\"\'\n\x13PushTaskInsResponse\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"F\n\x12PullTaskResRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"A\n\x13PullTaskResResponse\x12*\n\rtask_res_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskRes2\xd0\x02\n\x06\x44river\x12Y\n\x0e\x43reateWorkload\x12!.flwr.proto.CreateWorkloadRequest\x1a\".flwr.proto.CreateWorkloadResponse\"\x00\x12G\n\x08GetNodes\x12\x1b.flwr.proto.GetNodesRequest\x1a\x1c.flwr.proto.GetNodesResponse\"\x00\x12P\n\x0bPushTaskIns\x12\x1e.flwr.proto.PushTaskInsRequest\x1a\x1f.flwr.proto.PushTaskInsResponse\"\x00\x12P\n\x0bPullTaskRes\x12\x1e.flwr.proto.PullTaskResRequest\x1a\x1f.flwr.proto.PullTaskResResponse\"\x00\x62\x06proto3')
 
 
 
diff --git a/src/py/flwr/proto/driver_pb2.pyi b/src/py/flwr/proto/driver_pb2.pyi
index 486bddb0f76f..8b940972cb6d 100644
--- a/src/py/flwr/proto/driver_pb2.pyi
+++ b/src/py/flwr/proto/driver_pb2.pyi
@@ -23,10 +23,10 @@ global___CreateWorkloadRequest = CreateWorkloadRequest
 class CreateWorkloadResponse(google.protobuf.message.Message):
     DESCRIPTOR: google.protobuf.descriptor.Descriptor
     WORKLOAD_ID_FIELD_NUMBER: builtins.int
-    workload_id: typing.Text
+    workload_id: builtins.int
     def __init__(self,
         *,
-        workload_id: typing.Text = ...,
+        workload_id: builtins.int = ...,
         ) -> None: ...
     def ClearField(self, field_name: typing_extensions.Literal["workload_id",b"workload_id"]) -> None: ...
 global___CreateWorkloadResponse = CreateWorkloadResponse
@@ -35,10 +35,10 @@ class GetNodesRequest(google.protobuf.message.Message):
     """GetNodes messages"""
     DESCRIPTOR: google.protobuf.descriptor.Descriptor
     WORKLOAD_ID_FIELD_NUMBER: builtins.int
-    workload_id: typing.Text
+    workload_id: builtins.int
     def __init__(self,
         *,
-        workload_id: typing.Text = ...,
+        workload_id: builtins.int = ...,
         ) -> None: ...
     def ClearField(self, field_name: typing_extensions.Literal["workload_id",b"workload_id"]) -> None: ...
 global___GetNodesRequest = GetNodesRequest
diff --git a/src/py/flwr/proto/task_pb2.py b/src/py/flwr/proto/task_pb2.py
index 42d3952f61df..69bad48d0d37 100644
--- a/src/py/flwr/proto/task_pb2.py
+++ b/src/py/flwr/proto/task_pb2.py
@@ -16,7 +16,7 @@
 from flwr.proto import transport_pb2 as flwr_dot_proto_dot_transport__pb2
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/task.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x1a\x66lwr/proto/transport.proto\"\xbe\x02\n\x04Task\x12\"\n\x08producer\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\"\n\x08\x63onsumer\x18\x02 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x12\n\ncreated_at\x18\x03 \x01(\t\x12\x14\n\x0c\x64\x65livered_at\x18\x04 \x01(\t\x12\x0b\n\x03ttl\x18\x05 \x01(\t\x12\x10\n\x08\x61ncestry\x18\x06 \x03(\t\x12)\n\x02sa\x18\x07 \x01(\x0b\x32\x1d.flwr.proto.SecureAggregation\x12<\n\x15legacy_server_message\x18\x65 \x01(\x0b\x32\x19.flwr.proto.ServerMessageB\x02\x18\x01\x12<\n\x15legacy_client_message\x18\x66 \x01(\x0b\x32\x19.flwr.proto.ClientMessageB\x02\x18\x01\"a\n\x07TaskIns\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\t\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"a\n\x07TaskRes\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\t\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"\xf3\x03\n\x05Value\x12\x10\n\x06\x64ouble\x18\x01 \x01(\x01H\x00\x12\x10\n\x06sint64\x18\x02 \x01(\x12H\x00\x12\x0e\n\x04\x62ool\x18\x03 \x01(\x08H\x00\x12\x10\n\x06string\x18\x04 \x01(\tH\x00\x12\x0f\n\x05\x62ytes\x18\x05 \x01(\x0cH\x00\x12\x33\n\x0b\x64ouble_list\x18\x15 \x01(\x0b\x32\x1c.flwr.proto.Value.DoubleListH\x00\x12\x33\n\x0bsint64_list\x18\x16 \x01(\x0b\x32\x1c.flwr.proto.Value.Sint64ListH\x00\x12/\n\tbool_list\x18\x17 \x01(\x0b\x32\x1a.flwr.proto.Value.BoolListH\x00\x12\x33\n\x0bstring_list\x18\x18 \x01(\x0b\x32\x1c.flwr.proto.Value.StringListH\x00\x12\x31\n\nbytes_list\x18\x19 \x01(\x0b\x32\x1b.flwr.proto.Value.BytesListH\x00\x1a\x1a\n\nDoubleList\x12\x0c\n\x04vals\x18\x01 \x03(\x01\x1a\x1a\n\nSint64List\x12\x0c\n\x04vals\x18\x01 \x03(\x12\x1a\x18\n\x08\x42oolList\x12\x0c\n\x04vals\x18\x01 \x03(\x08\x1a\x1a\n\nStringList\x12\x0c\n\x04vals\x18\x01 \x03(\t\x1a\x19\n\tBytesList\x12\x0c\n\x04vals\x18\x01 \x03(\x0c\x42\x07\n\x05value\"\xa0\x01\n\x11SecureAggregation\x12\x44\n\x0cnamed_values\x18\x01 \x03(\x0b\x32..flwr.proto.SecureAggregation.NamedValuesEntry\x1a\x45\n\x10NamedValuesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12 \n\x05value\x18\x02 \x01(\x0b\x32\x11.flwr.proto.Value:\x02\x38\x01\x62\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/task.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x1a\x66lwr/proto/transport.proto\"\xbe\x02\n\x04Task\x12\"\n\x08producer\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\"\n\x08\x63onsumer\x18\x02 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x12\n\ncreated_at\x18\x03 \x01(\t\x12\x14\n\x0c\x64\x65livered_at\x18\x04 \x01(\t\x12\x0b\n\x03ttl\x18\x05 \x01(\t\x12\x10\n\x08\x61ncestry\x18\x06 \x03(\t\x12)\n\x02sa\x18\x07 \x01(\x0b\x32\x1d.flwr.proto.SecureAggregation\x12<\n\x15legacy_server_message\x18\x65 \x01(\x0b\x32\x19.flwr.proto.ServerMessageB\x02\x18\x01\x12<\n\x15legacy_client_message\x18\x66 \x01(\x0b\x32\x19.flwr.proto.ClientMessageB\x02\x18\x01\"a\n\x07TaskIns\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\x04\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"a\n\x07TaskRes\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\x04\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"\xf3\x03\n\x05Value\x12\x10\n\x06\x64ouble\x18\x01 \x01(\x01H\x00\x12\x10\n\x06sint64\x18\x02 \x01(\x12H\x00\x12\x0e\n\x04\x62ool\x18\x03 \x01(\x08H\x00\x12\x10\n\x06string\x18\x04 \x01(\tH\x00\x12\x0f\n\x05\x62ytes\x18\x05 \x01(\x0cH\x00\x12\x33\n\x0b\x64ouble_list\x18\x15 \x01(\x0b\x32\x1c.flwr.proto.Value.DoubleListH\x00\x12\x33\n\x0bsint64_list\x18\x16 \x01(\x0b\x32\x1c.flwr.proto.Value.Sint64ListH\x00\x12/\n\tbool_list\x18\x17 \x01(\x0b\x32\x1a.flwr.proto.Value.BoolListH\x00\x12\x33\n\x0bstring_list\x18\x18 \x01(\x0b\x32\x1c.flwr.proto.Value.StringListH\x00\x12\x31\n\nbytes_list\x18\x19 \x01(\x0b\x32\x1b.flwr.proto.Value.BytesListH\x00\x1a\x1a\n\nDoubleList\x12\x0c\n\x04vals\x18\x01 \x03(\x01\x1a\x1a\n\nSint64List\x12\x0c\n\x04vals\x18\x01 \x03(\x12\x1a\x18\n\x08\x42oolList\x12\x0c\n\x04vals\x18\x01 \x03(\x08\x1a\x1a\n\nStringList\x12\x0c\n\x04vals\x18\x01 \x03(\t\x1a\x19\n\tBytesList\x12\x0c\n\x04vals\x18\x01 \x03(\x0c\x42\x07\n\x05value\"\xa0\x01\n\x11SecureAggregation\x12\x44\n\x0cnamed_values\x18\x01 \x03(\x0b\x32..flwr.proto.SecureAggregation.NamedValuesEntry\x1a\x45\n\x10NamedValuesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12 \n\x05value\x18\x02 \x01(\x0b\x32\x11.flwr.proto.Value:\x02\x38\x01\x62\x06proto3')
 
 
 
diff --git a/src/py/flwr/proto/task_pb2.pyi b/src/py/flwr/proto/task_pb2.pyi
index dcd4686944bc..7cf96cb61edf 100644
--- a/src/py/flwr/proto/task_pb2.pyi
+++ b/src/py/flwr/proto/task_pb2.pyi
@@ -63,14 +63,14 @@ class TaskIns(google.protobuf.message.Message):
     TASK_FIELD_NUMBER: builtins.int
     task_id: typing.Text
     group_id: typing.Text
-    workload_id: typing.Text
+    workload_id: builtins.int
     @property
     def task(self) -> global___Task: ...
     def __init__(self,
         *,
         task_id: typing.Text = ...,
         group_id: typing.Text = ...,
-        workload_id: typing.Text = ...,
+        workload_id: builtins.int = ...,
         task: typing.Optional[global___Task] = ...,
         ) -> None: ...
     def HasField(self, field_name: typing_extensions.Literal["task",b"task"]) -> builtins.bool: ...
@@ -85,14 +85,14 @@ class TaskRes(google.protobuf.message.Message):
     TASK_FIELD_NUMBER: builtins.int
     task_id: typing.Text
     group_id: typing.Text
-    workload_id: typing.Text
+    workload_id: builtins.int
     @property
     def task(self) -> global___Task: ...
     def __init__(self,
         *,
         task_id: typing.Text = ...,
         group_id: typing.Text = ...,
-        workload_id: typing.Text = ...,
+        workload_id: builtins.int = ...,
         task: typing.Optional[global___Task] = ...,
         ) -> None: ...
     def HasField(self, field_name: typing_extensions.Literal["task",b"task"]) -> builtins.bool: ...
diff --git a/src/py/flwr/server/fleet/message_handler/message_handler_test.py b/src/py/flwr/server/fleet/message_handler/message_handler_test.py
index 10f678e3479e..da92b267f082 100644
--- a/src/py/flwr/server/fleet/message_handler/message_handler_test.py
+++ b/src/py/flwr/server/fleet/message_handler/message_handler_test.py
@@ -109,7 +109,7 @@ def test_push_task_res() -> None:
             TaskRes(
                 task_id="",
                 group_id="",
-                workload_id="",
+                workload_id=0,
                 task=Task(),
             ),
         ],
diff --git a/src/py/flwr/server/state/in_memory_state.py b/src/py/flwr/server/state/in_memory_state.py
index 075ba2cf304d..d6292571cd6d 100644
--- a/src/py/flwr/server/state/in_memory_state.py
+++ b/src/py/flwr/server/state/in_memory_state.py
@@ -32,7 +32,7 @@ class InMemoryState(State):
 
     def __init__(self) -> None:
         self.node_ids: Set[int] = set()
-        self.workload_ids: Set[str] = set()
+        self.workload_ids: Set[int] = set()
         self.task_ins_store: Dict[UUID, TaskIns] = {}
         self.task_res_store: Dict[UUID, TaskRes] = {}
 
@@ -194,7 +194,7 @@ def unregister_node(self, node_id: int) -> None:
             raise ValueError(f"Node {node_id} is not registered")
         self.node_ids.remove(node_id)
 
-    def get_nodes(self, workload_id: str) -> Set[int]:
+    def get_nodes(self, workload_id: int) -> Set[int]:
         """Return all available client nodes.
 
         Constraints
@@ -206,14 +206,13 @@ def get_nodes(self, workload_id: str) -> Set[int]:
             return set()
         return self.node_ids
 
-    def create_workload(self) -> str:
+    def create_workload(self) -> int:
         """Create one workload."""
-        # String representation of random integer from 0 to 9223372036854775807
-        random_workload_id: int = random.randrange(9223372036854775808)
-        workload_id = str(random_workload_id)
+        # Sample random integer from 0 to 9223372036854775807
+        workload_id: int = random.randrange(9223372036854775808)
 
         if workload_id not in self.workload_ids:
             self.workload_ids.add(workload_id)
             return workload_id
         log(ERROR, "Unexpected workload creation failure.")
-        return ""
+        return 0
diff --git a/src/py/flwr/server/state/sqlite_state.py b/src/py/flwr/server/state/sqlite_state.py
index e971c11da2f5..0c853409b844 100644
--- a/src/py/flwr/server/state/sqlite_state.py
+++ b/src/py/flwr/server/state/sqlite_state.py
@@ -39,7 +39,7 @@
 
 SQL_CREATE_TABLE_WORKLOAD = """
 CREATE TABLE IF NOT EXISTS workload(
-    workload_id TEXT UNIQUE
+    workload_id INTEGER UNIQUE
 );
 """
 
@@ -47,7 +47,7 @@
 CREATE TABLE IF NOT EXISTS task_ins(
     task_id                 TEXT UNIQUE,
     group_id                TEXT,
-    workload_id             TEXT,
+    workload_id             INTEGER,
     producer_anonymous      BOOLEAN,
     producer_node_id        INTEGER,
     consumer_anonymous      BOOLEAN,
@@ -67,7 +67,7 @@
 CREATE TABLE IF NOT EXISTS task_res(
     task_id                 TEXT UNIQUE,
     group_id                TEXT,
-    workload_id             TEXT,
+    workload_id             INTEGER,
     producer_anonymous      BOOLEAN,
     producer_node_id        INTEGER,
     consumer_anonymous      BOOLEAN,
@@ -479,7 +479,7 @@ def unregister_node(self, node_id: int) -> None:
         query = "DELETE FROM node WHERE node_id = :node_id;"
         self.query(query, {"node_id": node_id})
 
-    def get_nodes(self, workload_id: str) -> Set[int]:
+    def get_nodes(self, workload_id: int) -> Set[int]:
         """Retrieve all currently stored node IDs as a set.
 
         Constraints
@@ -498,11 +498,10 @@ def get_nodes(self, workload_id: str) -> Set[int]:
         result: Set[int] = {row["node_id"] for row in rows}
         return result
 
-    def create_workload(self) -> str:
+    def create_workload(self) -> int:
         """Create one workload and store it in state."""
-        # String representation of random integer from 0 to 9223372036854775807
-        random_workload_id: int = random.randrange(9223372036854775808)
-        workload_id = str(random_workload_id)
+        # Sample random integer from 0 to 9223372036854775807
+        workload_id: int = random.randrange(9223372036854775808)
 
         # Check conflicts
         query = "SELECT COUNT(*) FROM workload WHERE workload_id = ?;"
@@ -512,7 +511,7 @@ def create_workload(self) -> str:
             self.query(query, {"workload_id": workload_id})
             return workload_id
         log(ERROR, "Unexpected workload creation failure.")
-        return ""
+        return 0
 
 
 def dict_factory(
diff --git a/src/py/flwr/server/state/sqlite_state_test.py b/src/py/flwr/server/state/sqlite_state_test.py
index e3bb72e34118..b9c0df9ed134 100644
--- a/src/py/flwr/server/state/sqlite_state_test.py
+++ b/src/py/flwr/server/state/sqlite_state_test.py
@@ -27,7 +27,7 @@ class SqliteStateTest(unittest.TestCase):
     def test_ins_res_to_dict(self) -> None:
         """Check if all required keys are included in return value."""
         # Prepare
-        ins_res = create_task_ins(consumer_node_id=1, anonymous=True, workload_id="")
+        ins_res = create_task_ins(consumer_node_id=1, anonymous=True, workload_id=0)
         expected_keys = [
             "task_id",
             "group_id",
diff --git a/src/py/flwr/server/state/state.py b/src/py/flwr/server/state/state.py
index cfd68c589b6e..a0b9e663f637 100644
--- a/src/py/flwr/server/state/state.py
+++ b/src/py/flwr/server/state/state.py
@@ -140,7 +140,7 @@ def unregister_node(self, node_id: int) -> None:
         """Remove `node_id` from state."""
 
     @abc.abstractmethod
-    def get_nodes(self, workload_id: str) -> Set[int]:
+    def get_nodes(self, workload_id: int) -> Set[int]:
         """Retrieve all currently stored node IDs as a set.
 
         Constraints
@@ -150,5 +150,5 @@ def get_nodes(self, workload_id: str) -> Set[int]:
         """
 
     @abc.abstractmethod
-    def create_workload(self) -> str:
+    def create_workload(self) -> int:
         """Create one workload."""
diff --git a/src/py/flwr/server/state/state_test.py b/src/py/flwr/server/state/state_test.py
index e80bd55352ed..bc3015ba5cc2 100644
--- a/src/py/flwr/server/state/state_test.py
+++ b/src/py/flwr/server/state/state_test.py
@@ -283,7 +283,7 @@ def test_task_ins_store_invalid_workload_id_and_fail(self) -> None:
         # Prepare
         state: State = self.state_factory()
         task_ins = create_task_ins(
-            consumer_node_id=0, anonymous=True, workload_id="I'm invalid"
+            consumer_node_id=0, anonymous=True, workload_id=61016
         )
 
         # Execute
@@ -362,7 +362,7 @@ def test_get_nodes_invalid_workload_id(self) -> None:
         # Prepare
         state: State = self.state_factory()
         state.create_workload()
-        invalid_workload_id = ""
+        invalid_workload_id = 61016
         node_id = 2
 
         # Execute
@@ -420,7 +420,7 @@ def test_num_task_res(self) -> None:
 def create_task_ins(
     consumer_node_id: int,
     anonymous: bool,
-    workload_id: str,
+    workload_id: int,
     delivered_at: str = "",
 ) -> TaskIns:
     """Create a TaskIns for testing."""
@@ -448,7 +448,7 @@ def create_task_res(
     producer_node_id: int,
     anonymous: bool,
     ancestry: List[str],
-    workload_id: str,
+    workload_id: int,
 ) -> TaskRes:
     """Create a TaskRes for testing."""
     task_res = TaskRes(
diff --git a/src/py/flwr/server/utils/validator_test.py b/src/py/flwr/server/utils/validator_test.py
index 533e3a236572..54840731048f 100644
--- a/src/py/flwr/server/utils/validator_test.py
+++ b/src/py/flwr/server/utils/validator_test.py
@@ -135,7 +135,7 @@ def create_task_ins(
     task = TaskIns(
         task_id="",
         group_id="",
-        workload_id="",
+        workload_id=0,
         task=Task(
             delivered_at=delivered_at,
             producer=Node(node_id=0, anonymous=True),
@@ -162,7 +162,7 @@ def create_task_res(
     task_res = TaskRes(
         task_id="",
         group_id="",
-        workload_id="",
+        workload_id=0,
         task=Task(
             producer=Node(node_id=producer_node_id, anonymous=anonymous),
             consumer=Node(node_id=0, anonymous=True),

From 857db302070ebce33b23ad529de75f181b35b645 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Sat, 7 Oct 2023 13:33:50 +0100
Subject: [PATCH 33/60] Prevent simulation without CPU resources set (#2448)

---
 doc/source/ref-changelog.md   |  2 +-
 src/py/flwr/simulation/app.py | 18 +++++++++++++++++-
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index d42f782bb11f..7355b4123347 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -20,7 +20,7 @@
 
 - **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))
 
-- **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331))
+- **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331), [#2448](https://github.com/adap/flower/pull/2448))
 
 - **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))
 
diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py
index 6fcfb82be2c0..5c7a3e7423a3 100644
--- a/src/py/flwr/simulation/app.py
+++ b/src/py/flwr/simulation/app.py
@@ -18,6 +18,7 @@
 import sys
 import threading
 import traceback
+import warnings
 from logging import ERROR, INFO
 from typing import Any, Dict, List, Optional, Type, Union
 
@@ -68,7 +69,7 @@
 """
 
 
-def start_simulation(  # pylint: disable=too-many-arguments
+def start_simulation(  # pylint: disable=too-many-arguments,too-many-statements
     *,
     client_fn: ClientFn,
     num_clients: Optional[int] = None,
@@ -214,6 +215,12 @@ def start_simulation(  # pylint: disable=too-many-arguments
         cluster_resources,
     )
 
+    log(
+        INFO,
+        "Optimize your simulation with Flower VCE: "
+        "https://flower.dev/docs/framework/how-to-run-simulations.html",
+    )
+
     # Log the resources that a single client will be able to use
     if client_resources is None:
         log(
@@ -222,6 +229,15 @@ def start_simulation(  # pylint: disable=too-many-arguments
         )
         client_resources = {"num_cpus": 1, "num_gpus": 0.0}
 
+    # Each client needs at the very least one CPU
+    if "num_cpus" not in client_resources:
+        warnings.warn(
+            "No `num_cpus` specified in `client_resources`. "
+            "Using `num_cpus=1` for each client.",
+            stacklevel=2,
+        )
+        client_resources["num_cpus"] = 1
+
     log(
         INFO,
         "Flower VCE: Resources for each Virtual Client: %s",

From e559ef220cefb6ec584b7ab3ad06f5816fc24362 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Sat, 7 Oct 2023 13:39:29 +0100
Subject: [PATCH 34/60] Simplify imports in driver module (#2483)

---
 src/py/flwr/driver/driver.py | 38 ++++++++++++++++++++----------------
 1 file changed, 21 insertions(+), 17 deletions(-)

diff --git a/src/py/flwr/driver/driver.py b/src/py/flwr/driver/driver.py
index 64e61ec4cb61..130cd2bbc707 100644
--- a/src/py/flwr/driver/driver.py
+++ b/src/py/flwr/driver/driver.py
@@ -23,7 +23,17 @@
 from flwr.common import EventType, event
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log
-from flwr.proto import driver_pb2, driver_pb2_grpc
+from flwr.proto.driver_pb2 import (
+    CreateWorkloadRequest,
+    CreateWorkloadResponse,
+    GetNodesRequest,
+    GetNodesResponse,
+    PullTaskResRequest,
+    PullTaskResResponse,
+    PushTaskInsRequest,
+    PushTaskInsResponse,
+)
+from flwr.proto.driver_pb2_grpc import DriverStub
 
 DEFAULT_SERVER_ADDRESS_DRIVER = "[::]:9091"
 
@@ -46,7 +56,7 @@ def __init__(
         self.driver_service_address = driver_service_address
         self.certificates = certificates
         self.channel: Optional[grpc.Channel] = None
-        self.stub: Optional[driver_pb2_grpc.DriverStub] = None
+        self.stub: Optional[DriverStub] = None
 
     def connect(self) -> None:
         """Connect to the Driver API."""
@@ -58,7 +68,7 @@ def connect(self) -> None:
             server_address=self.driver_service_address,
             root_certificates=self.certificates,
         )
-        self.stub = driver_pb2_grpc.DriverStub(self.channel)
+        self.stub = DriverStub(self.channel)
         log(INFO, "[Driver] Connected to %s", self.driver_service_address)
 
     def disconnect(self) -> None:
@@ -73,9 +83,7 @@ def disconnect(self) -> None:
         channel.close()
         log(INFO, "[Driver] Disconnected")
 
-    def create_workload(
-        self, req: driver_pb2.CreateWorkloadRequest
-    ) -> driver_pb2.CreateWorkloadResponse:
+    def create_workload(self, req: CreateWorkloadRequest) -> CreateWorkloadResponse:
         """Request for workload ID."""
         # Check if channel is open
         if self.stub is None:
@@ -83,10 +91,10 @@ def create_workload(
             raise Exception("`Driver` instance not connected")
 
         # Call Driver API
-        res: driver_pb2.CreateWorkloadResponse = self.stub.CreateWorkload(request=req)
+        res: CreateWorkloadResponse = self.stub.CreateWorkload(request=req)
         return res
 
-    def get_nodes(self, req: driver_pb2.GetNodesRequest) -> driver_pb2.GetNodesResponse:
+    def get_nodes(self, req: GetNodesRequest) -> GetNodesResponse:
         """Get client IDs."""
         # Check if channel is open
         if self.stub is None:
@@ -94,12 +102,10 @@ def get_nodes(self, req: driver_pb2.GetNodesRequest) -> driver_pb2.GetNodesRespo
             raise Exception("`Driver` instance not connected")
 
         # Call Driver API
-        res: driver_pb2.GetNodesResponse = self.stub.GetNodes(request=req)
+        res: GetNodesResponse = self.stub.GetNodes(request=req)
         return res
 
-    def push_task_ins(
-        self, req: driver_pb2.PushTaskInsRequest
-    ) -> driver_pb2.PushTaskInsResponse:
+    def push_task_ins(self, req: PushTaskInsRequest) -> PushTaskInsResponse:
         """Schedule tasks."""
         # Check if channel is open
         if self.stub is None:
@@ -107,12 +113,10 @@ def push_task_ins(
             raise Exception("`Driver` instance not connected")
 
         # Call Driver API
-        res: driver_pb2.PushTaskInsResponse = self.stub.PushTaskIns(request=req)
+        res: PushTaskInsResponse = self.stub.PushTaskIns(request=req)
         return res
 
-    def pull_task_res(
-        self, req: driver_pb2.PullTaskResRequest
-    ) -> driver_pb2.PullTaskResResponse:
+    def pull_task_res(self, req: PullTaskResRequest) -> PullTaskResResponse:
         """Get task results."""
         # Check if channel is open
         if self.stub is None:
@@ -120,5 +124,5 @@ def pull_task_res(
             raise Exception("`Driver` instance not connected")
 
         # Call Driver API
-        res: driver_pb2.PullTaskResResponse = self.stub.PullTaskRes(request=req)
+        res: PullTaskResResponse = self.stub.PullTaskRes(request=req)
         return res

From 6a23be111a0ed0b98cd5471ca0091c9b8c273c34 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Sat, 7 Oct 2023 15:37:24 +0200
Subject: [PATCH 35/60] Add check wheel contents (#2119)

Co-authored-by: Daniel J. Beutel <daniel@adap.com>
---
 dev/test-wheel.sh | 7 +++++++
 pyproject.toml    | 1 +
 2 files changed, 8 insertions(+)

diff --git a/dev/test-wheel.sh b/dev/test-wheel.sh
index 68ce876955eb..98363ff35550 100755
--- a/dev/test-wheel.sh
+++ b/dev/test-wheel.sh
@@ -21,4 +21,11 @@ if [ -z "$(python -m pyroma ./ | grep 'Final rating: 10/10')" ]; then
 fi
 echo "Pyroma wheel check: done"
 
+echo "Check-wheel-content wheel check: start"
+# The ignore W002 is "Wheel contains duplicate files"
+# It is required because of the autogenerated proto files
+# There is no file-based ignore for this tool
+check-wheel-contents ./dist/ --ignore W002
+echo "Check-wheel-content wheel check: done"
+
 echo "- All wheel checks passed"
diff --git a/pyproject.toml b/pyproject.toml
index dfdd75ba11ab..d8d00cad1d54 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -117,6 +117,7 @@ mdformat-beautysh = "==0.1.1"
 mdformat-myst = "==0.1.5"
 twine = "==4.0.2"
 pyroma = "==4.2"
+check-wheel-contents = "==0.4.0"
 GitPython = "==3.1.32"
 
 [tool.isort]

From b9ae24aac0c7207eaf65ef3f800aef25df5925a7 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Sat, 7 Oct 2023 15:53:29 +0100
Subject: [PATCH 36/60] Change ID types from uint64 to sint64 (#2485)

---
 src/proto/flwr/proto/driver.proto | 4 ++--
 src/proto/flwr/proto/node.proto   | 2 +-
 src/proto/flwr/proto/task.proto   | 4 ++--
 src/py/flwr/proto/driver_pb2.py   | 2 +-
 src/py/flwr/proto/node_pb2.py     | 2 +-
 src/py/flwr/proto/task_pb2.py     | 2 +-
 6 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/proto/flwr/proto/driver.proto b/src/proto/flwr/proto/driver.proto
index 1caaad88a0da..226862df5572 100644
--- a/src/proto/flwr/proto/driver.proto
+++ b/src/proto/flwr/proto/driver.proto
@@ -36,10 +36,10 @@ service Driver {
 
 // CreateWorkload
 message CreateWorkloadRequest {}
-message CreateWorkloadResponse { uint64 workload_id = 1; }
+message CreateWorkloadResponse { sint64 workload_id = 1; }
 
 // GetNodes messages
-message GetNodesRequest { uint64 workload_id = 1; }
+message GetNodesRequest { sint64 workload_id = 1; }
 message GetNodesResponse { repeated Node nodes = 1; }
 
 // PushTaskIns messages
diff --git a/src/proto/flwr/proto/node.proto b/src/proto/flwr/proto/node.proto
index a7ea6262b4c5..92263e0bd5ba 100644
--- a/src/proto/flwr/proto/node.proto
+++ b/src/proto/flwr/proto/node.proto
@@ -18,6 +18,6 @@ syntax = "proto3";
 package flwr.proto;
 
 message Node {
-  uint64 node_id = 1;
+  sint64 node_id = 1;
   bool anonymous = 2;
 }
diff --git a/src/proto/flwr/proto/task.proto b/src/proto/flwr/proto/task.proto
index d87fb39c2637..26385f18ac61 100644
--- a/src/proto/flwr/proto/task.proto
+++ b/src/proto/flwr/proto/task.proto
@@ -36,14 +36,14 @@ message Task {
 message TaskIns {
   string task_id = 1;
   string group_id = 2;
-  uint64 workload_id = 3;
+  sint64 workload_id = 3;
   Task task = 4;
 }
 
 message TaskRes {
   string task_id = 1;
   string group_id = 2;
-  uint64 workload_id = 3;
+  sint64 workload_id = 3;
   Task task = 4;
 }
 
diff --git a/src/py/flwr/proto/driver_pb2.py b/src/py/flwr/proto/driver_pb2.py
index 6ac066d7eab3..c138507e03e9 100644
--- a/src/py/flwr/proto/driver_pb2.py
+++ b/src/py/flwr/proto/driver_pb2.py
@@ -16,7 +16,7 @@
 from flwr.proto import task_pb2 as flwr_dot_proto_dot_task__pb2
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x17\x66lwr/proto/driver.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x15\x66lwr/proto/task.proto\"\x17\n\x15\x43reateWorkloadRequest\"-\n\x16\x43reateWorkloadResponse\x12\x13\n\x0bworkload_id\x18\x01 \x01(\x04\"&\n\x0fGetNodesRequest\x12\x13\n\x0bworkload_id\x18\x01 \x01(\x04\"3\n\x10GetNodesResponse\x12\x1f\n\x05nodes\x18\x01 \x03(\x0b\x32\x10.flwr.proto.Node\"@\n\x12PushTaskInsRequest\x12*\n\rtask_ins_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskIns\"\'\n\x13PushTaskInsResponse\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"F\n\x12PullTaskResRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"A\n\x13PullTaskResResponse\x12*\n\rtask_res_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskRes2\xd0\x02\n\x06\x44river\x12Y\n\x0e\x43reateWorkload\x12!.flwr.proto.CreateWorkloadRequest\x1a\".flwr.proto.CreateWorkloadResponse\"\x00\x12G\n\x08GetNodes\x12\x1b.flwr.proto.GetNodesRequest\x1a\x1c.flwr.proto.GetNodesResponse\"\x00\x12P\n\x0bPushTaskIns\x12\x1e.flwr.proto.PushTaskInsRequest\x1a\x1f.flwr.proto.PushTaskInsResponse\"\x00\x12P\n\x0bPullTaskRes\x12\x1e.flwr.proto.PullTaskResRequest\x1a\x1f.flwr.proto.PullTaskResResponse\"\x00\x62\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x17\x66lwr/proto/driver.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x15\x66lwr/proto/task.proto\"\x17\n\x15\x43reateWorkloadRequest\"-\n\x16\x43reateWorkloadResponse\x12\x13\n\x0bworkload_id\x18\x01 \x01(\x12\"&\n\x0fGetNodesRequest\x12\x13\n\x0bworkload_id\x18\x01 \x01(\x12\"3\n\x10GetNodesResponse\x12\x1f\n\x05nodes\x18\x01 \x03(\x0b\x32\x10.flwr.proto.Node\"@\n\x12PushTaskInsRequest\x12*\n\rtask_ins_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskIns\"\'\n\x13PushTaskInsResponse\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"F\n\x12PullTaskResRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"A\n\x13PullTaskResResponse\x12*\n\rtask_res_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskRes2\xd0\x02\n\x06\x44river\x12Y\n\x0e\x43reateWorkload\x12!.flwr.proto.CreateWorkloadRequest\x1a\".flwr.proto.CreateWorkloadResponse\"\x00\x12G\n\x08GetNodes\x12\x1b.flwr.proto.GetNodesRequest\x1a\x1c.flwr.proto.GetNodesResponse\"\x00\x12P\n\x0bPushTaskIns\x12\x1e.flwr.proto.PushTaskInsRequest\x1a\x1f.flwr.proto.PushTaskInsResponse\"\x00\x12P\n\x0bPullTaskRes\x12\x1e.flwr.proto.PullTaskResRequest\x1a\x1f.flwr.proto.PullTaskResResponse\"\x00\x62\x06proto3')
 
 
 
diff --git a/src/py/flwr/proto/node_pb2.py b/src/py/flwr/proto/node_pb2.py
index 7ecbc08bf4a9..9d91900d8f53 100644
--- a/src/py/flwr/proto/node_pb2.py
+++ b/src/py/flwr/proto/node_pb2.py
@@ -14,7 +14,7 @@
 
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/node.proto\x12\nflwr.proto\"*\n\x04Node\x12\x0f\n\x07node_id\x18\x01 \x01(\x04\x12\x11\n\tanonymous\x18\x02 \x01(\x08\x62\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/node.proto\x12\nflwr.proto\"*\n\x04Node\x12\x0f\n\x07node_id\x18\x01 \x01(\x12\x12\x11\n\tanonymous\x18\x02 \x01(\x08\x62\x06proto3')
 
 
 
diff --git a/src/py/flwr/proto/task_pb2.py b/src/py/flwr/proto/task_pb2.py
index 69bad48d0d37..6d8cf8fd3656 100644
--- a/src/py/flwr/proto/task_pb2.py
+++ b/src/py/flwr/proto/task_pb2.py
@@ -16,7 +16,7 @@
 from flwr.proto import transport_pb2 as flwr_dot_proto_dot_transport__pb2
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/task.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x1a\x66lwr/proto/transport.proto\"\xbe\x02\n\x04Task\x12\"\n\x08producer\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\"\n\x08\x63onsumer\x18\x02 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x12\n\ncreated_at\x18\x03 \x01(\t\x12\x14\n\x0c\x64\x65livered_at\x18\x04 \x01(\t\x12\x0b\n\x03ttl\x18\x05 \x01(\t\x12\x10\n\x08\x61ncestry\x18\x06 \x03(\t\x12)\n\x02sa\x18\x07 \x01(\x0b\x32\x1d.flwr.proto.SecureAggregation\x12<\n\x15legacy_server_message\x18\x65 \x01(\x0b\x32\x19.flwr.proto.ServerMessageB\x02\x18\x01\x12<\n\x15legacy_client_message\x18\x66 \x01(\x0b\x32\x19.flwr.proto.ClientMessageB\x02\x18\x01\"a\n\x07TaskIns\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\x04\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"a\n\x07TaskRes\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\x04\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"\xf3\x03\n\x05Value\x12\x10\n\x06\x64ouble\x18\x01 \x01(\x01H\x00\x12\x10\n\x06sint64\x18\x02 \x01(\x12H\x00\x12\x0e\n\x04\x62ool\x18\x03 \x01(\x08H\x00\x12\x10\n\x06string\x18\x04 \x01(\tH\x00\x12\x0f\n\x05\x62ytes\x18\x05 \x01(\x0cH\x00\x12\x33\n\x0b\x64ouble_list\x18\x15 \x01(\x0b\x32\x1c.flwr.proto.Value.DoubleListH\x00\x12\x33\n\x0bsint64_list\x18\x16 \x01(\x0b\x32\x1c.flwr.proto.Value.Sint64ListH\x00\x12/\n\tbool_list\x18\x17 \x01(\x0b\x32\x1a.flwr.proto.Value.BoolListH\x00\x12\x33\n\x0bstring_list\x18\x18 \x01(\x0b\x32\x1c.flwr.proto.Value.StringListH\x00\x12\x31\n\nbytes_list\x18\x19 \x01(\x0b\x32\x1b.flwr.proto.Value.BytesListH\x00\x1a\x1a\n\nDoubleList\x12\x0c\n\x04vals\x18\x01 \x03(\x01\x1a\x1a\n\nSint64List\x12\x0c\n\x04vals\x18\x01 \x03(\x12\x1a\x18\n\x08\x42oolList\x12\x0c\n\x04vals\x18\x01 \x03(\x08\x1a\x1a\n\nStringList\x12\x0c\n\x04vals\x18\x01 \x03(\t\x1a\x19\n\tBytesList\x12\x0c\n\x04vals\x18\x01 \x03(\x0c\x42\x07\n\x05value\"\xa0\x01\n\x11SecureAggregation\x12\x44\n\x0cnamed_values\x18\x01 \x03(\x0b\x32..flwr.proto.SecureAggregation.NamedValuesEntry\x1a\x45\n\x10NamedValuesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12 \n\x05value\x18\x02 \x01(\x0b\x32\x11.flwr.proto.Value:\x02\x38\x01\x62\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/task.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x1a\x66lwr/proto/transport.proto\"\xbe\x02\n\x04Task\x12\"\n\x08producer\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\"\n\x08\x63onsumer\x18\x02 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x12\n\ncreated_at\x18\x03 \x01(\t\x12\x14\n\x0c\x64\x65livered_at\x18\x04 \x01(\t\x12\x0b\n\x03ttl\x18\x05 \x01(\t\x12\x10\n\x08\x61ncestry\x18\x06 \x03(\t\x12)\n\x02sa\x18\x07 \x01(\x0b\x32\x1d.flwr.proto.SecureAggregation\x12<\n\x15legacy_server_message\x18\x65 \x01(\x0b\x32\x19.flwr.proto.ServerMessageB\x02\x18\x01\x12<\n\x15legacy_client_message\x18\x66 \x01(\x0b\x32\x19.flwr.proto.ClientMessageB\x02\x18\x01\"a\n\x07TaskIns\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"a\n\x07TaskRes\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"\xf3\x03\n\x05Value\x12\x10\n\x06\x64ouble\x18\x01 \x01(\x01H\x00\x12\x10\n\x06sint64\x18\x02 \x01(\x12H\x00\x12\x0e\n\x04\x62ool\x18\x03 \x01(\x08H\x00\x12\x10\n\x06string\x18\x04 \x01(\tH\x00\x12\x0f\n\x05\x62ytes\x18\x05 \x01(\x0cH\x00\x12\x33\n\x0b\x64ouble_list\x18\x15 \x01(\x0b\x32\x1c.flwr.proto.Value.DoubleListH\x00\x12\x33\n\x0bsint64_list\x18\x16 \x01(\x0b\x32\x1c.flwr.proto.Value.Sint64ListH\x00\x12/\n\tbool_list\x18\x17 \x01(\x0b\x32\x1a.flwr.proto.Value.BoolListH\x00\x12\x33\n\x0bstring_list\x18\x18 \x01(\x0b\x32\x1c.flwr.proto.Value.StringListH\x00\x12\x31\n\nbytes_list\x18\x19 \x01(\x0b\x32\x1b.flwr.proto.Value.BytesListH\x00\x1a\x1a\n\nDoubleList\x12\x0c\n\x04vals\x18\x01 \x03(\x01\x1a\x1a\n\nSint64List\x12\x0c\n\x04vals\x18\x01 \x03(\x12\x1a\x18\n\x08\x42oolList\x12\x0c\n\x04vals\x18\x01 \x03(\x08\x1a\x1a\n\nStringList\x12\x0c\n\x04vals\x18\x01 \x03(\t\x1a\x19\n\tBytesList\x12\x0c\n\x04vals\x18\x01 \x03(\x0c\x42\x07\n\x05value\"\xa0\x01\n\x11SecureAggregation\x12\x44\n\x0cnamed_values\x18\x01 \x03(\x0b\x32..flwr.proto.SecureAggregation.NamedValuesEntry\x1a\x45\n\x10NamedValuesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12 \n\x05value\x18\x02 \x01(\x0b\x32\x11.flwr.proto.Value:\x02\x38\x01\x62\x06proto3')
 
 
 

From 6df3e86c8b46a818b113d293fa4f95f304ffa8e4 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Sat, 7 Oct 2023 17:51:00 +0200
Subject: [PATCH 37/60] Fix return types in Strategy docs (#2432)

---
 doc/source/ref-changelog.md             | 4 ++++
 src/py/flwr/server/strategy/strategy.py | 4 ++--
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 7355b4123347..eefd215503f8 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -8,6 +8,10 @@
 
 ### What's new?
 
+- **Fix the incorrect return types of Strategy** ([#2432](https://github.com/adap/flower/pull/2432/files))
+
+  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.
+
 - **Unify client API** ([#2303](https://github.com/adap/flower/pull/2303))
 
   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.
diff --git a/src/py/flwr/server/strategy/strategy.py b/src/py/flwr/server/strategy/strategy.py
index 95d61993e02e..a2c774fe2506 100644
--- a/src/py/flwr/server/strategy/strategy.py
+++ b/src/py/flwr/server/strategy/strategy.py
@@ -95,7 +95,7 @@ def aggregate_fit(
 
         Returns
         -------
-        parameters : Optional[Parameters]
+        parameters : Tuple[Optional[Parameters], Dict[str, Scalar]]
             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
@@ -156,7 +156,7 @@ def aggregate_evaluate(
 
         Returns
         -------
-        aggregation_result : Optional[float]
+        aggregation_result : Tuple[Optional[float], Dict[str, Scalar]]
             The aggregated evaluation result. Aggregation typically uses some variant
             of a weighted average.
         """

From c4a88765debffa20b4952c2f31f72c4617644df7 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Sat, 7 Oct 2023 17:17:28 +0100
Subject: [PATCH 38/60] Move `node_id` generation into `State` (#2401)

---
 .../fleet/grpc_bidi/driver_client_manager.py  | 15 ++++-------
 .../fleet/message_handler/message_handler.py  | 12 +++------
 .../message_handler/message_handler_test.py   | 20 +++++++-------
 src/py/flwr/server/state/in_memory_state.py   | 27 +++++++++++--------
 src/py/flwr/server/state/sqlite_state.py      | 24 +++++++++++------
 src/py/flwr/server/state/state.py             |  6 ++---
 src/py/flwr/server/state/state_test.py        | 22 +++++++--------
 7 files changed, 64 insertions(+), 62 deletions(-)

diff --git a/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py b/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py
index 55542d87654b..88265b0e1123 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py
@@ -15,7 +15,6 @@
 """Flower DriverClientManager."""
 
 
-import random
 import threading
 from typing import Dict, List, Optional, Set, Tuple
 
@@ -71,13 +70,9 @@ def register(self, client: ClientProxy) -> bool:
         if client.cid in self.nodes:
             return False
 
-        # Generate random integer ID
-        random_node_id: int = random.randrange(9223372036854775808)
-        client.node_id = random_node_id
-
-        # Register node_id in with State
+        # Create node in State
         state: State = self.state_factory.state()
-        state.register_node(node_id=random_node_id)
+        client.node_id = state.create_node()
 
         # Create and start the instruction scheduler
         ins_scheduler = InsScheduler(
@@ -87,7 +82,7 @@ def register(self, client: ClientProxy) -> bool:
         ins_scheduler.start()
 
         # Store cid, node_id, and InsScheduler
-        self.nodes[client.cid] = (random_node_id, ins_scheduler)
+        self.nodes[client.cid] = (client.node_id, ins_scheduler)
 
         with self._cv:
             self._cv.notify_all()
@@ -108,9 +103,9 @@ def unregister(self, client: ClientProxy) -> None:
             del self.nodes[client.cid]
             ins_scheduler.stop()
 
-            # Unregister node_id in with State
+            # Delete node_id in State
             state: State = self.state_factory.state()
-            state.unregister_node(node_id=node_id)
+            state.delete_node(node_id=node_id)
 
             with self._cv:
                 self._cv.notify_all()
diff --git a/src/py/flwr/server/fleet/message_handler/message_handler.py b/src/py/flwr/server/fleet/message_handler/message_handler.py
index 08b315f9b5de..da27976f112d 100644
--- a/src/py/flwr/server/fleet/message_handler/message_handler.py
+++ b/src/py/flwr/server/fleet/message_handler/message_handler.py
@@ -15,7 +15,6 @@
 """Fleet API message handlers."""
 
 
-import random
 from typing import List, Optional
 from uuid import UUID
 
@@ -40,12 +39,9 @@ def create_node(
     state: State,
 ) -> CreateNodeResponse:
     """."""
-    # Generate random node_id
-    random_node_id: int = random.randrange(9223372036854775808)
-
-    # Update state
-    state.register_node(node_id=random_node_id)
-    return CreateNodeResponse(node=Node(node_id=random_node_id, anonymous=False))
+    # Create node
+    node_id = state.create_node()
+    return CreateNodeResponse(node=Node(node_id=node_id, anonymous=False))
 
 
 def delete_node(request: DeleteNodeRequest, state: State) -> DeleteNodeResponse:
@@ -55,7 +51,7 @@ def delete_node(request: DeleteNodeRequest, state: State) -> DeleteNodeResponse:
         return DeleteNodeResponse()
 
     # Update state
-    state.unregister_node(node_id=request.node.node_id)
+    state.delete_node(node_id=request.node.node_id)
     return DeleteNodeResponse()
 
 
diff --git a/src/py/flwr/server/fleet/message_handler/message_handler_test.py b/src/py/flwr/server/fleet/message_handler/message_handler_test.py
index da92b267f082..e5d889adeb3f 100644
--- a/src/py/flwr/server/fleet/message_handler/message_handler_test.py
+++ b/src/py/flwr/server/fleet/message_handler/message_handler_test.py
@@ -39,8 +39,8 @@ def test_create_node() -> None:
     create_node(request=request, state=state)
 
     # Assert
-    state.register_node.assert_called_once()
-    state.unregister_node.assert_not_called()
+    state.create_node.assert_called_once()
+    state.delete_node.assert_not_called()
     state.store_task_ins.assert_not_called()
     state.get_task_ins.assert_not_called()
     state.store_task_res.assert_not_called()
@@ -57,8 +57,8 @@ def test_delete_node_failure() -> None:
     delete_node(request=request, state=state)
 
     # Assert
-    state.register_node.assert_not_called()
-    state.unregister_node.assert_not_called()
+    state.create_node.assert_not_called()
+    state.delete_node.assert_not_called()
     state.store_task_ins.assert_not_called()
     state.get_task_ins.assert_not_called()
     state.store_task_res.assert_not_called()
@@ -75,8 +75,8 @@ def test_delete_node_success() -> None:
     delete_node(request=request, state=state)
 
     # Assert
-    state.register_node.assert_not_called()
-    state.unregister_node.assert_called_once()
+    state.create_node.assert_not_called()
+    state.delete_node.assert_called_once()
     state.store_task_ins.assert_not_called()
     state.get_task_ins.assert_not_called()
     state.store_task_res.assert_not_called()
@@ -93,8 +93,8 @@ def test_pull_task_ins() -> None:
     pull_task_ins(request=request, state=state)
 
     # Assert
-    state.register_node.assert_not_called()
-    state.unregister_node.assert_not_called()
+    state.create_node.assert_not_called()
+    state.delete_node.assert_not_called()
     state.store_task_ins.assert_not_called()
     state.get_task_ins.assert_called_once()
     state.store_task_res.assert_not_called()
@@ -120,8 +120,8 @@ def test_push_task_res() -> None:
     push_task_res(request=request, state=state)
 
     # Assert
-    state.register_node.assert_not_called()
-    state.unregister_node.assert_not_called()
+    state.create_node.assert_not_called()
+    state.delete_node.assert_not_called()
     state.store_task_ins.assert_not_called()
     state.get_task_ins.assert_not_called()
     state.store_task_res.assert_called_once()
diff --git a/src/py/flwr/server/state/in_memory_state.py b/src/py/flwr/server/state/in_memory_state.py
index d6292571cd6d..85515809bd4e 100644
--- a/src/py/flwr/server/state/in_memory_state.py
+++ b/src/py/flwr/server/state/in_memory_state.py
@@ -15,7 +15,7 @@
 """In-memory State implementation."""
 
 
-import random
+import os
 from datetime import datetime, timedelta
 from logging import ERROR
 from typing import Dict, List, Optional, Set
@@ -182,16 +182,21 @@ def num_task_res(self) -> int:
         """
         return len(self.task_res_store)
 
-    def register_node(self, node_id: int) -> None:
-        """Register a client node."""
-        if node_id in self.node_ids:
-            raise ValueError(f"Node {node_id} is already registered")
-        self.node_ids.add(node_id)
+    def create_node(self) -> int:
+        """Create, store in state, and return `node_id`."""
+        # Sample a random int64 as node_id
+        node_id: int = int.from_bytes(os.urandom(8), "little", signed=True)
 
-    def unregister_node(self, node_id: int) -> None:
-        """Unregister a client node."""
         if node_id not in self.node_ids:
-            raise ValueError(f"Node {node_id} is not registered")
+            self.node_ids.add(node_id)
+            return node_id
+        log(ERROR, "Unexpected node registration failure.")
+        return 0
+
+    def delete_node(self, node_id: int) -> None:
+        """Delete a client node."""
+        if node_id not in self.node_ids:
+            raise ValueError(f"Node {node_id} not found")
         self.node_ids.remove(node_id)
 
     def get_nodes(self, workload_id: int) -> Set[int]:
@@ -208,8 +213,8 @@ def get_nodes(self, workload_id: int) -> Set[int]:
 
     def create_workload(self) -> int:
         """Create one workload."""
-        # Sample random integer from 0 to 9223372036854775807
-        workload_id: int = random.randrange(9223372036854775808)
+        # Sample a random int64 as workload_id
+        workload_id: int = int.from_bytes(os.urandom(8), "little", signed=True)
 
         if workload_id not in self.workload_ids:
             self.workload_ids.add(workload_id)
diff --git a/src/py/flwr/server/state/sqlite_state.py b/src/py/flwr/server/state/sqlite_state.py
index 0c853409b844..2f878443ccd1 100644
--- a/src/py/flwr/server/state/sqlite_state.py
+++ b/src/py/flwr/server/state/sqlite_state.py
@@ -15,7 +15,7 @@
 """SQLite based implemenation of server state."""
 
 
-import random
+import os
 import re
 import sqlite3
 from datetime import datetime, timedelta
@@ -469,13 +469,21 @@ def delete_tasks(self, task_ids: Set[UUID]) -> None:
 
         return None
 
-    def register_node(self, node_id: int) -> None:
-        """Store `node_id` in state."""
+    def create_node(self) -> int:
+        """Create, store in state, and return `node_id`."""
+        # Sample a random int64 as node_id
+        node_id: int = int.from_bytes(os.urandom(8), "little", signed=True)
+
         query = "INSERT INTO node VALUES(:node_id);"
-        self.query(query, {"node_id": node_id})
+        try:
+            self.query(query, {"node_id": node_id})
+        except sqlite3.IntegrityError:
+            log(ERROR, "Unexpected node registration failure.")
+            return 0
+        return node_id
 
-    def unregister_node(self, node_id: int) -> None:
-        """Remove `node_id` from state."""
+    def delete_node(self, node_id: int) -> None:
+        """Delete a client node."""
         query = "DELETE FROM node WHERE node_id = :node_id;"
         self.query(query, {"node_id": node_id})
 
@@ -500,8 +508,8 @@ def get_nodes(self, workload_id: int) -> Set[int]:
 
     def create_workload(self) -> int:
         """Create one workload and store it in state."""
-        # Sample random integer from 0 to 9223372036854775807
-        workload_id: int = random.randrange(9223372036854775808)
+        # Sample a random int64 as workload_id
+        workload_id: int = int.from_bytes(os.urandom(8), "little", signed=True)
 
         # Check conflicts
         query = "SELECT COUNT(*) FROM workload WHERE workload_id = ?;"
diff --git a/src/py/flwr/server/state/state.py b/src/py/flwr/server/state/state.py
index a0b9e663f637..1e08d9e4f5b7 100644
--- a/src/py/flwr/server/state/state.py
+++ b/src/py/flwr/server/state/state.py
@@ -132,11 +132,11 @@ def delete_tasks(self, task_ids: Set[UUID]) -> None:
         """Delete all delivered TaskIns/TaskRes pairs."""
 
     @abc.abstractmethod
-    def register_node(self, node_id: int) -> None:
-        """Store `node_id` in state."""
+    def create_node(self) -> int:
+        """Create, store in state, and return `node_id`."""
 
     @abc.abstractmethod
-    def unregister_node(self, node_id: int) -> None:
+    def delete_node(self, node_id: int) -> None:
         """Remove `node_id` from state."""
 
     @abc.abstractmethod
diff --git a/src/py/flwr/server/state/state_test.py b/src/py/flwr/server/state/state_test.py
index bc3015ba5cc2..11d6d2386a4e 100644
--- a/src/py/flwr/server/state/state_test.py
+++ b/src/py/flwr/server/state/state_test.py
@@ -326,32 +326,31 @@ def test_node_ids_initial_state(self) -> None:
         # Assert
         assert len(retrieved_node_ids) == 0
 
-    def test_register_node_and_get_nodes(self) -> None:
-        """Test registering a client node."""
+    def test_create_node_and_get_nodes(self) -> None:
+        """Test creating a client node."""
         # Prepare
         state: State = self.state_factory()
         workload_id = state.create_workload()
-        node_ids = list(range(1, 11))
+        node_ids = []
 
         # Execute
-        for i in node_ids:
-            state.register_node(i)
+        for _ in range(10):
+            node_ids.append(state.create_node())
         retrieved_node_ids = state.get_nodes(workload_id)
 
         # Assert
         for i in retrieved_node_ids:
             assert i in node_ids
 
-    def test_unregister_node(self) -> None:
-        """Test unregistering a client node."""
+    def test_delete_node(self) -> None:
+        """Test deleting a client node."""
         # Prepare
         state: State = self.state_factory()
         workload_id = state.create_workload()
-        node_id = 2
+        node_id = state.create_node()
 
         # Execute
-        state.register_node(node_id)
-        state.unregister_node(node_id)
+        state.delete_node(node_id)
         retrieved_node_ids = state.get_nodes(workload_id)
 
         # Assert
@@ -363,10 +362,9 @@ def test_get_nodes_invalid_workload_id(self) -> None:
         state: State = self.state_factory()
         state.create_workload()
         invalid_workload_id = 61016
-        node_id = 2
+        state.create_node()
 
         # Execute
-        state.register_node(node_id)
         retrieved_node_ids = state.get_nodes(invalid_workload_id)
 
         # Assert

From 015dddac2de95ee53a6584153a24a3da6ef36eb6 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.dev>
Date: Sun, 8 Oct 2023 21:21:05 +0200
Subject: [PATCH 39/60] Upgrade mypy to 1.5.1 (#2487)

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

diff --git a/pyproject.toml b/pyproject.toml
index d8d00cad1d54..11910c305f9c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -86,7 +86,7 @@ clang-format = "==16.0.6"
 isort = "==5.11.5"
 black = { version = "==23.3.0", extras = ["jupyter"] }
 docformatter = "==1.7.5"
-mypy = "==1.4.1"
+mypy = "==1.5.1"
 pylint = "==2.13.9"
 flake8 = "==3.9.2"
 pytest = "==7.4.0"

From aa135fb4db445d8bc2030092ee51e6a32b825a56 Mon Sep 17 00:00:00 2001
From: Taner Topal <taner@flower.dev>
Date: Sun, 8 Oct 2023 16:04:31 -0700
Subject: [PATCH 40/60] Fix issue with if condition in GitHub action (#2490)

---
 .github/workflows/docs.yml | 2 +-
 .github/workflows/e2e.yml  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index da3a67fc155e..f8efc09e5120 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -32,7 +32,7 @@ jobs:
       - name: Build docs
         run: ./dev/build-docs.sh
       - name: Deploy docs
-        if: github.ref == 'refs/heads/main' && github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
+        if: ${{ github.ref == 'refs/heads/main' && github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
         env:
           AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 3a58503ea66e..4168f5938f42 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -30,7 +30,7 @@ jobs:
       - name: Test wheel
         run: ./dev/test-wheel.sh
       - name: Upload wheel
-        if: github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
+        if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
         id: upload
         env:
           AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}

From 2c860469f560f7836c94d3129ddb05fd70e80f9a Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Mon, 9 Oct 2023 08:01:53 +0200
Subject: [PATCH 41/60] Fix E2E workflow (#2491)

---
 .github/workflows/e2e.yml   | 4 ++--
 .github/workflows/swift.yml | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 4168f5938f42..238b9c0b862d 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -121,7 +121,7 @@ jobs:
       - name: Install dependencies
         run: python -m poetry install
       - name: Install Flower wheel from artifact store
-        if: github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
+        if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
         run: |
           python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Download dataset
@@ -156,7 +156,7 @@ jobs:
         run: |
           python -m poetry install
       - name: Install Flower wheel from artifact store
-        if: github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
+        if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
         run: |
           python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Cache Datasets
diff --git a/.github/workflows/swift.yml b/.github/workflows/swift.yml
index 9edd7f7ff6e1..7420566b85f5 100644
--- a/.github/workflows/swift.yml
+++ b/.github/workflows/swift.yml
@@ -40,7 +40,7 @@ jobs:
 
   deploy_docs:
     needs: "build_docs"
-    if: github.ref == 'refs/heads/main' && github.repository == 'adap/flower' && ${{ !github.event.pull_request.head.repo.fork }}
+    if: ${{ github.ref == 'refs/heads/main' && github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
     runs-on: macos-latest
     name: Deploy docs 
     steps:

From ebca16520631efa34787d43dd7ffae21af70cb76 Mon Sep 17 00:00:00 2001
From: Taner Topal <taner@flower.dev>
Date: Mon, 9 Oct 2023 00:10:05 -0700
Subject: [PATCH 42/60] Fix the copyright notice in existing files (#2489)

Co-authored-by: Daniel J. Beutel <daniel@flower.dev>
---
 baselines/doc/source/conf.py                                  | 4 ++--
 baselines/flwr_baselines/flwr_baselines/dataset/__init__.py   | 2 +-
 .../flwr_baselines/dataset/generator/__init__.py              | 2 +-
 .../flwr_baselines/flwr_baselines/dataset/utils/__init__.py   | 2 +-
 .../flwr_baselines/flwr_baselines/dataset/utils/common.py     | 2 +-
 .../flwr_baselines/dataset/utils/common_test.py               | 2 +-
 dev/aws-ami-bootstrap-tf.sh                                   | 2 +-
 dev/aws-ami-bootstrap-torch.sh                                | 2 +-
 dev/build.sh                                                  | 2 +-
 dev/check-protos.sh                                           | 2 +-
 dev/deploy-docs.sh                                            | 2 +-
 dev/deploy-swift-docs.sh                                      | 2 +-
 dev/publish-nightly.sh                                        | 2 +-
 dev/publish.sh                                                | 2 +-
 doc/locales/fr/LC_MESSAGES/sphinx.po                          | 2 +-
 doc/source/conf.py                                            | 4 ++--
 .../client/flower_tflite/src/main/proto/transport.proto       | 2 +-
 examples/android/client/app/src/main/proto/transport.proto    | 2 +-
 examples/doc/source/conf.py                                   | 4 ++--
 src/docker/build.sh                                           | 2 +-
 src/proto/flwr/proto/driver.proto                             | 2 +-
 src/proto/flwr/proto/fleet.proto                              | 2 +-
 src/proto/flwr/proto/node.proto                               | 2 +-
 src/proto/flwr/proto/task.proto                               | 2 +-
 src/proto/flwr/proto/transport.proto                          | 2 +-
 src/py/flwr/__init__.py                                       | 2 +-
 src/py/flwr/__init___test.py                                  | 2 +-
 src/py/flwr/client/__init__.py                                | 2 +-
 src/py/flwr/client/app.py                                     | 2 +-
 src/py/flwr/client/app_test.py                                | 2 +-
 src/py/flwr/client/client.py                                  | 2 +-
 src/py/flwr/client/client_test.py                             | 2 +-
 src/py/flwr/client/dpfedavg_numpy_client.py                   | 2 +-
 src/py/flwr/client/grpc_client/__init__.py                    | 2 +-
 src/py/flwr/client/grpc_client/connection.py                  | 2 +-
 src/py/flwr/client/grpc_client/connection_test.py             | 2 +-
 src/py/flwr/client/grpc_rere_client/__init__.py               | 2 +-
 src/py/flwr/client/grpc_rere_client/connection.py             | 2 +-
 src/py/flwr/client/message_handler/__init__.py                | 2 +-
 src/py/flwr/client/message_handler/message_handler.py         | 2 +-
 src/py/flwr/client/message_handler/message_handler_test.py    | 2 +-
 src/py/flwr/client/message_handler/task_handler.py            | 2 +-
 src/py/flwr/client/message_handler/task_handler_test.py       | 2 +-
 src/py/flwr/client/numpy_client.py                            | 2 +-
 src/py/flwr/client/numpy_client_test.py                       | 2 +-
 src/py/flwr/client/rest_client/__init__.py                    | 2 +-
 src/py/flwr/client/rest_client/connection.py                  | 2 +-
 src/py/flwr/client/secure_aggregation/__init__.py             | 2 +-
 src/py/flwr/client/secure_aggregation/handler.py              | 2 +-
 src/py/flwr/client/secure_aggregation/secaggplus_handler.py   | 2 +-
 .../flwr/client/secure_aggregation/secaggplus_handler_test.py | 2 +-
 src/py/flwr/common/__init__.py                                | 2 +-
 src/py/flwr/common/address.py                                 | 2 +-
 src/py/flwr/common/address_test.py                            | 2 +-
 src/py/flwr/common/constant.py                                | 2 +-
 src/py/flwr/common/date.py                                    | 2 +-
 src/py/flwr/common/dp.py                                      | 2 +-
 src/py/flwr/common/grpc.py                                    | 2 +-
 src/py/flwr/common/logger.py                                  | 2 +-
 src/py/flwr/common/parameter.py                               | 2 +-
 src/py/flwr/common/parameter_test.py                          | 2 +-
 src/py/flwr/common/secure_aggregation/__init__.py             | 2 +-
 src/py/flwr/common/secure_aggregation/crypto/__init__.py      | 2 +-
 src/py/flwr/common/secure_aggregation/crypto/shamir.py        | 2 +-
 .../common/secure_aggregation/crypto/symmetric_encryption.py  | 2 +-
 src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py  | 2 +-
 src/py/flwr/common/secure_aggregation/quantization.py         | 2 +-
 src/py/flwr/common/secure_aggregation/secaggplus_constants.py | 2 +-
 src/py/flwr/common/secure_aggregation/secaggplus_utils.py     | 2 +-
 src/py/flwr/common/serde.py                                   | 2 +-
 src/py/flwr/common/serde_test.py                              | 2 +-
 src/py/flwr/common/telemetry.py                               | 2 +-
 src/py/flwr/common/telemetry_test.py                          | 2 +-
 src/py/flwr/common/typing.py                                  | 2 +-
 src/py/flwr/driver/__init__.py                                | 2 +-
 src/py/flwr/driver/app.py                                     | 2 +-
 src/py/flwr/driver/app_test.py                                | 2 +-
 src/py/flwr/driver/driver.py                                  | 2 +-
 src/py/flwr/driver/driver_client_proxy.py                     | 2 +-
 src/py/flwr/driver/driver_client_proxy_test.py                | 2 +-
 src/py/flwr/driver/driver_test.py                             | 2 +-
 src/py/flwr/proto/__init__.py                                 | 2 +-
 src/py/flwr/server/__init__.py                                | 2 +-
 src/py/flwr/server/app.py                                     | 2 +-
 src/py/flwr/server/client_manager.py                          | 2 +-
 src/py/flwr/server/client_manager_test.py                     | 2 +-
 src/py/flwr/server/client_proxy.py                            | 2 +-
 src/py/flwr/server/client_proxy_test.py                       | 2 +-
 src/py/flwr/server/criterion.py                               | 2 +-
 src/py/flwr/server/criterion_test.py                          | 2 +-
 src/py/flwr/server/driver/__init__.py                         | 2 +-
 src/py/flwr/server/driver/driver_servicer.py                  | 2 +-
 src/py/flwr/server/driver/driver_servicer_test.py             | 2 +-
 src/py/flwr/server/fleet/__init__.py                          | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/__init__.py                | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py   | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer.py | 2 +-
 .../server/fleet/grpc_bidi/flower_service_servicer_test.py    | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/grpc_bridge.py             | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/grpc_bridge_test.py        | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy.py       | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy_test.py  | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/grpc_server.py             | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/grpc_server_test.py        | 2 +-
 src/py/flwr/server/fleet/grpc_bidi/ins_scheduler.py           | 2 +-
 src/py/flwr/server/fleet/grpc_rere/__init__.py                | 2 +-
 src/py/flwr/server/fleet/grpc_rere/fleet_servicer.py          | 2 +-
 src/py/flwr/server/fleet/message_handler/__init__.py          | 2 +-
 src/py/flwr/server/fleet/message_handler/message_handler.py   | 2 +-
 .../flwr/server/fleet/message_handler/message_handler_test.py | 2 +-
 src/py/flwr/server/fleet/rest_rere/__init__.py                | 2 +-
 src/py/flwr/server/fleet/rest_rere/rest_api.py                | 2 +-
 src/py/flwr/server/history.py                                 | 2 +-
 src/py/flwr/server/history_test.py                            | 2 +-
 src/py/flwr/server/server.py                                  | 2 +-
 src/py/flwr/server/server_test.py                             | 2 +-
 src/py/flwr/server/state/__init__.py                          | 2 +-
 src/py/flwr/server/state/in_memory_state.py                   | 2 +-
 src/py/flwr/server/state/sqlite_state.py                      | 2 +-
 src/py/flwr/server/state/sqlite_state_test.py                 | 2 +-
 src/py/flwr/server/state/state.py                             | 2 +-
 src/py/flwr/server/state/state_factory.py                     | 2 +-
 src/py/flwr/server/state/state_test.py                        | 2 +-
 src/py/flwr/server/strategy/__init__.py                       | 2 +-
 src/py/flwr/server/strategy/aggregate.py                      | 2 +-
 src/py/flwr/server/strategy/aggregate_test.py                 | 2 +-
 src/py/flwr/server/strategy/dpfedavg_adaptive.py              | 2 +-
 src/py/flwr/server/strategy/dpfedavg_fixed.py                 | 2 +-
 src/py/flwr/server/strategy/fault_tolerant_fedavg.py          | 2 +-
 src/py/flwr/server/strategy/fault_tolerant_fedavg_test.py     | 2 +-
 src/py/flwr/server/strategy/fedadagrad.py                     | 2 +-
 src/py/flwr/server/strategy/fedadagrad_test.py                | 2 +-
 src/py/flwr/server/strategy/fedadam.py                        | 2 +-
 src/py/flwr/server/strategy/fedavg.py                         | 2 +-
 src/py/flwr/server/strategy/fedavg_android.py                 | 2 +-
 src/py/flwr/server/strategy/fedavg_test.py                    | 2 +-
 src/py/flwr/server/strategy/fedavgm.py                        | 2 +-
 src/py/flwr/server/strategy/fedavgm_test.py                   | 2 +-
 src/py/flwr/server/strategy/fedmedian.py                      | 2 +-
 src/py/flwr/server/strategy/fedmedian_test.py                 | 2 +-
 src/py/flwr/server/strategy/fedopt.py                         | 2 +-
 src/py/flwr/server/strategy/fedprox.py                        | 2 +-
 src/py/flwr/server/strategy/fedxgb_nn_avg.py                  | 2 +-
 src/py/flwr/server/strategy/fedyogi.py                        | 2 +-
 src/py/flwr/server/strategy/krum.py                           | 2 +-
 src/py/flwr/server/strategy/krum_test.py                      | 2 +-
 src/py/flwr/server/strategy/multikrum_test.py                 | 2 +-
 src/py/flwr/server/strategy/qfedavg.py                        | 2 +-
 src/py/flwr/server/strategy/strategy.py                       | 2 +-
 src/py/flwr/server/utils/__init__.py                          | 2 +-
 src/py/flwr/server/utils/tensorboard.py                       | 2 +-
 src/py/flwr/server/utils/tensorboard_test.py                  | 2 +-
 src/py/flwr/server/utils/validator.py                         | 2 +-
 src/py/flwr/server/utils/validator_test.py                    | 2 +-
 src/py/flwr/simulation/__init__.py                            | 2 +-
 src/py/flwr/simulation/app.py                                 | 2 +-
 src/py/flwr/simulation/ray_transport/__init__.py              | 2 +-
 src/py/flwr/simulation/ray_transport/ray_client_proxy.py      | 2 +-
 src/py/flwr_example/__init__.py                               | 2 +-
 src/py/flwr_example/pytorch_cifar/__init__.py                 | 2 +-
 src/py/flwr_example/pytorch_cifar/cifar.py                    | 2 +-
 src/py/flwr_example/pytorch_cifar/cifar_test.py               | 2 +-
 src/py/flwr_example/pytorch_cifar/client.py                   | 2 +-
 src/py/flwr_example/pytorch_cifar/run-clients.sh              | 2 +-
 src/py/flwr_example/pytorch_cifar/run-server.sh               | 2 +-
 src/py/flwr_example/pytorch_cifar/server.py                   | 2 +-
 src/py/flwr_example/pytorch_imagenet/__init__.py              | 2 +-
 src/py/flwr_example/pytorch_imagenet/client.py                | 2 +-
 src/py/flwr_example/pytorch_imagenet/imagenet.py              | 2 +-
 src/py/flwr_example/pytorch_imagenet/run-clients.sh           | 2 +-
 src/py/flwr_example/pytorch_imagenet/run-server.sh            | 2 +-
 src/py/flwr_example/pytorch_imagenet/server.py                | 2 +-
 src/py/flwr_example/pytorch_save_weights/__init__.py          | 2 +-
 src/py/flwr_example/pytorch_save_weights/cifar.py             | 2 +-
 src/py/flwr_example/pytorch_save_weights/client.py            | 2 +-
 src/py/flwr_example/pytorch_save_weights/run-clients.sh       | 2 +-
 src/py/flwr_example/pytorch_save_weights/run-server.sh        | 2 +-
 src/py/flwr_example/pytorch_save_weights/server.py            | 2 +-
 src/py/flwr_example/quickstart_pytorch/client.py              | 2 +-
 src/py/flwr_example/quickstart_pytorch/mnist.py               | 2 +-
 src/py/flwr_example/quickstart_pytorch/run-clients.sh         | 2 +-
 src/py/flwr_example/quickstart_pytorch/run-server.sh          | 2 +-
 src/py/flwr_example/quickstart_pytorch/server.py              | 2 +-
 src/py/flwr_example/quickstart_tensorflow/run-clients.sh      | 2 +-
 src/py/flwr_example/quickstart_tensorflow/run-server.sh       | 2 +-
 src/py/flwr_example/tensorflow_fashion_mnist/__init__.py      | 2 +-
 src/py/flwr_example/tensorflow_fashion_mnist/client.py        | 2 +-
 src/py/flwr_example/tensorflow_fashion_mnist/download.py      | 2 +-
 src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist.py | 2 +-
 .../tensorflow_fashion_mnist/fashion_mnist_test.py            | 2 +-
 src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh   | 2 +-
 src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh    | 2 +-
 src/py/flwr_example/tensorflow_fashion_mnist/server.py        | 2 +-
 src/py/flwr_experimental/__init__.py                          | 2 +-
 src/py/flwr_experimental/baseline/__init__.py                 | 2 +-
 src/py/flwr_experimental/baseline/command.py                  | 2 +-
 src/py/flwr_experimental/baseline/common/__init__.py          | 2 +-
 src/py/flwr_experimental/baseline/common/client.py            | 2 +-
 src/py/flwr_experimental/baseline/common/common.py            | 2 +-
 src/py/flwr_experimental/baseline/common/data.py              | 2 +-
 src/py/flwr_experimental/baseline/config/__init__.py          | 2 +-
 src/py/flwr_experimental/baseline/config/config.py            | 2 +-
 src/py/flwr_experimental/baseline/config/config_test.py       | 2 +-
 src/py/flwr_experimental/baseline/dataset/__init__.py         | 2 +-
 src/py/flwr_experimental/baseline/dataset/dataset.py          | 2 +-
 src/py/flwr_experimental/baseline/dataset/dataset_test.py     | 2 +-
 .../baseline/dataset/tf_cifar_partitioned.py                  | 2 +-
 .../baseline/dataset/tf_cifar_partitioned_test.py             | 2 +-
 .../baseline/dataset/tf_fashion_mnist_partitioned.py          | 2 +-
 .../baseline/dataset/tf_fashion_mnist_partitioned_test.py     | 2 +-
 .../baseline/dataset/tf_hotkey_partitioned.py                 | 2 +-
 src/py/flwr_experimental/baseline/ip.py                       | 2 +-
 src/py/flwr_experimental/baseline/model/__init__.py           | 2 +-
 src/py/flwr_experimental/baseline/model/cnn.py                | 2 +-
 src/py/flwr_experimental/baseline/model/cnn_test.py           | 2 +-
 src/py/flwr_experimental/baseline/model/resnet.py             | 2 +-
 src/py/flwr_experimental/baseline/plot/__init__.py            | 2 +-
 src/py/flwr_experimental/baseline/plot/plot.py                | 2 +-
 src/py/flwr_experimental/baseline/run.py                      | 2 +-
 src/py/flwr_experimental/baseline/run.sh                      | 2 +-
 src/py/flwr_experimental/baseline/setting.py                  | 2 +-
 src/py/flwr_experimental/baseline/tf_cifar/__init__.py        | 2 +-
 src/py/flwr_experimental/baseline/tf_cifar/client.py          | 2 +-
 src/py/flwr_experimental/baseline/tf_cifar/download.py        | 2 +-
 src/py/flwr_experimental/baseline/tf_cifar/server.py          | 2 +-
 src/py/flwr_experimental/baseline/tf_cifar/settings.py        | 2 +-
 .../flwr_experimental/baseline/tf_fashion_mnist/__init__.py   | 2 +-
 src/py/flwr_experimental/baseline/tf_fashion_mnist/client.py  | 2 +-
 .../flwr_experimental/baseline/tf_fashion_mnist/download.py   | 2 +-
 .../flwr_experimental/baseline/tf_fashion_mnist/fn_plots.py   | 2 +-
 .../flwr_experimental/baseline/tf_fashion_mnist/gen_plots.py  | 2 +-
 src/py/flwr_experimental/baseline/tf_fashion_mnist/server.py  | 2 +-
 .../flwr_experimental/baseline/tf_fashion_mnist/settings.py   | 2 +-
 src/py/flwr_experimental/baseline/tf_hotkey/__init__.py       | 2 +-
 src/py/flwr_experimental/baseline/tf_hotkey/client.py         | 2 +-
 src/py/flwr_experimental/baseline/tf_hotkey/download.py       | 2 +-
 src/py/flwr_experimental/baseline/tf_hotkey/server.py         | 2 +-
 src/py/flwr_experimental/baseline/tf_hotkey/settings.py       | 2 +-
 src/py/flwr_experimental/logserver/__init__.py                | 2 +-
 src/py/flwr_experimental/logserver/__main__.py                | 2 +-
 src/py/flwr_experimental/logserver/server.py                  | 2 +-
 src/py/flwr_experimental/logserver/server_test.py             | 2 +-
 src/py/flwr_experimental/ops/__init__.py                      | 2 +-
 src/py/flwr_experimental/ops/cluster.py                       | 2 +-
 src/py/flwr_experimental/ops/cluster_test.py                  | 2 +-
 src/py/flwr_experimental/ops/compute/__init__.py              | 2 +-
 src/py/flwr_experimental/ops/compute/adapter.py               | 2 +-
 src/py/flwr_experimental/ops/compute/docker_adapter.py        | 2 +-
 src/py/flwr_experimental/ops/compute/docker_adapter_test.py   | 2 +-
 src/py/flwr_experimental/ops/compute/ec2_adapter.py           | 2 +-
 src/py/flwr_experimental/ops/compute/ec2_adapter_test.py      | 2 +-
 src/py/flwr_experimental/ops/instance.py                      | 2 +-
 src/py/flwr_tool/__init__.py                                  | 2 +-
 src/py/flwr_tool/init_py_check.py                             | 2 +-
 src/py/flwr_tool/protoc.py                                    | 2 +-
 src/py/flwr_tool/protoc_test.py                               | 2 +-
 src/swift/flwr/Sources/Flower/FlowerProto/transport.pb.swift  | 2 +-
 257 files changed, 260 insertions(+), 260 deletions(-)

diff --git a/baselines/doc/source/conf.py b/baselines/doc/source/conf.py
index b9b89b24f58a..7ba9e8c9cc7d 100644
--- a/baselines/doc/source/conf.py
+++ b/baselines/doc/source/conf.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
@@ -32,7 +32,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "Flower"
-copyright = "2022 Adap GmbH"
+copyright = "2022 Flower Labs GmbH"
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
diff --git a/baselines/flwr_baselines/flwr_baselines/dataset/__init__.py b/baselines/flwr_baselines/flwr_baselines/dataset/__init__.py
index 3ad427581343..f4a2aff9b164 100644
--- a/baselines/flwr_baselines/flwr_baselines/dataset/__init__.py
+++ b/baselines/flwr_baselines/flwr_baselines/dataset/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/baselines/flwr_baselines/flwr_baselines/dataset/generator/__init__.py b/baselines/flwr_baselines/flwr_baselines/dataset/generator/__init__.py
index 362158276cc5..622dd6e648d8 100644
--- a/baselines/flwr_baselines/flwr_baselines/dataset/generator/__init__.py
+++ b/baselines/flwr_baselines/flwr_baselines/dataset/generator/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/baselines/flwr_baselines/flwr_baselines/dataset/utils/__init__.py b/baselines/flwr_baselines/flwr_baselines/dataset/utils/__init__.py
index ee4d0d1a1cb2..b45109671b5d 100644
--- a/baselines/flwr_baselines/flwr_baselines/dataset/utils/__init__.py
+++ b/baselines/flwr_baselines/flwr_baselines/dataset/utils/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/baselines/flwr_baselines/flwr_baselines/dataset/utils/common.py b/baselines/flwr_baselines/flwr_baselines/dataset/utils/common.py
index 817201563fde..124c82588100 100644
--- a/baselines/flwr_baselines/flwr_baselines/dataset/utils/common.py
+++ b/baselines/flwr_baselines/flwr_baselines/dataset/utils/common.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/baselines/flwr_baselines/flwr_baselines/dataset/utils/common_test.py b/baselines/flwr_baselines/flwr_baselines/dataset/utils/common_test.py
index c919c60b12d6..6c32627d87af 100644
--- a/baselines/flwr_baselines/flwr_baselines/dataset/utils/common_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/dataset/utils/common_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/dev/aws-ami-bootstrap-tf.sh b/dev/aws-ami-bootstrap-tf.sh
index b7fae90be1a0..bece7d21f1a0 100755
--- a/dev/aws-ami-bootstrap-tf.sh
+++ b/dev/aws-ami-bootstrap-tf.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/dev/aws-ami-bootstrap-torch.sh b/dev/aws-ami-bootstrap-torch.sh
index 511f73890e70..1c44cb09673d 100755
--- a/dev/aws-ami-bootstrap-torch.sh
+++ b/dev/aws-ami-bootstrap-torch.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/dev/build.sh b/dev/build.sh
index a24fed8223d9..a257e9402dc3 100755
--- a/dev/build.sh
+++ b/dev/build.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/dev/check-protos.sh b/dev/check-protos.sh
index 3bd0ca1f767f..4e9927bbab0b 100755
--- a/dev/check-protos.sh
+++ b/dev/check-protos.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/dev/deploy-docs.sh b/dev/deploy-docs.sh
index 75b5010cc119..69fbd567baf3 100755
--- a/dev/deploy-docs.sh
+++ b/dev/deploy-docs.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/dev/deploy-swift-docs.sh b/dev/deploy-swift-docs.sh
index 36298fc0a5ae..5abe618f83d3 100755
--- a/dev/deploy-swift-docs.sh
+++ b/dev/deploy-swift-docs.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/dev/publish-nightly.sh b/dev/publish-nightly.sh
index a1378fb47eb8..2d2e82325fb9 100755
--- a/dev/publish-nightly.sh
+++ b/dev/publish-nightly.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/dev/publish.sh b/dev/publish.sh
index 7f368a6c24ba..fb4df1694530 100755
--- a/dev/publish.sh
+++ b/dev/publish.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/doc/locales/fr/LC_MESSAGES/sphinx.po b/doc/locales/fr/LC_MESSAGES/sphinx.po
index a82fcd1bfae2..28669f60bab6 100644
--- a/doc/locales/fr/LC_MESSAGES/sphinx.po
+++ b/doc/locales/fr/LC_MESSAGES/sphinx.po
@@ -1,5 +1,5 @@
 # SOME DESCRIPTIVE TITLE.
-# Copyright (C) 2022 Adap GmbH
+# Copyright (C) 2022 Flower Labs GmbH
 # This file is distributed under the same license as the Flower package.
 # FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
 #
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 28787a7aca8c..87860e2c6e7b 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
@@ -81,7 +81,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "Flower"
-copyright = "2022 Adap GmbH"
+copyright = "2022 Flower Labs GmbH"
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
diff --git a/examples/android-kotlin/client/flower_tflite/src/main/proto/transport.proto b/examples/android-kotlin/client/flower_tflite/src/main/proto/transport.proto
index 82ae5081bd02..1ba3a97639f5 100644
--- a/examples/android-kotlin/client/flower_tflite/src/main/proto/transport.proto
+++ b/examples/android-kotlin/client/flower_tflite/src/main/proto/transport.proto
@@ -1,4 +1,4 @@
-// Copyright 2020 Adap GmbH. All Rights Reserved.
+// 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.
diff --git a/examples/android/client/app/src/main/proto/transport.proto b/examples/android/client/app/src/main/proto/transport.proto
index 82ae5081bd02..1ba3a97639f5 100644
--- a/examples/android/client/app/src/main/proto/transport.proto
+++ b/examples/android/client/app/src/main/proto/transport.proto
@@ -1,4 +1,4 @@
-// Copyright 2020 Adap GmbH. All Rights Reserved.
+// 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.
diff --git a/examples/doc/source/conf.py b/examples/doc/source/conf.py
index c57f11a8c763..dcb1788ea336 100644
--- a/examples/doc/source/conf.py
+++ b/examples/doc/source/conf.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
@@ -23,7 +23,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "Flower"
-copyright = "2022 Adap GmbH"
+copyright = "2022 Flower Labs GmbH"
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
diff --git a/src/docker/build.sh b/src/docker/build.sh
index 428ed5c20ac3..ea6b643c1fb2 100755
--- a/src/docker/build.sh
+++ b/src/docker/build.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/proto/flwr/proto/driver.proto b/src/proto/flwr/proto/driver.proto
index 226862df5572..eb948217a4de 100644
--- a/src/proto/flwr/proto/driver.proto
+++ b/src/proto/flwr/proto/driver.proto
@@ -1,4 +1,4 @@
-// Copyright 2022 Adap GmbH. All Rights Reserved.
+// Copyright 2022 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.
diff --git a/src/proto/flwr/proto/fleet.proto b/src/proto/flwr/proto/fleet.proto
index 460fd7e6b372..c900a3b1148d 100644
--- a/src/proto/flwr/proto/fleet.proto
+++ b/src/proto/flwr/proto/fleet.proto
@@ -1,4 +1,4 @@
-// Copyright 2022 Adap GmbH. All Rights Reserved.
+// Copyright 2022 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.
diff --git a/src/proto/flwr/proto/node.proto b/src/proto/flwr/proto/node.proto
index 92263e0bd5ba..e61d44f0f783 100644
--- a/src/proto/flwr/proto/node.proto
+++ b/src/proto/flwr/proto/node.proto
@@ -1,4 +1,4 @@
-// Copyright 2022 Adap GmbH. All Rights Reserved.
+// Copyright 2022 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.
diff --git a/src/proto/flwr/proto/task.proto b/src/proto/flwr/proto/task.proto
index 26385f18ac61..2205ef2815c8 100644
--- a/src/proto/flwr/proto/task.proto
+++ b/src/proto/flwr/proto/task.proto
@@ -1,4 +1,4 @@
-// Copyright 2022 Adap GmbH. All Rights Reserved.
+// Copyright 2022 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.
diff --git a/src/proto/flwr/proto/transport.proto b/src/proto/flwr/proto/transport.proto
index ccbb1a2751b4..17a285ebe44b 100644
--- a/src/proto/flwr/proto/transport.proto
+++ b/src/proto/flwr/proto/transport.proto
@@ -1,4 +1,4 @@
-// Copyright 2020 Adap GmbH. All Rights Reserved.
+// 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.
diff --git a/src/py/flwr/__init__.py b/src/py/flwr/__init__.py
index bf3145c67410..d3cbf00747a4 100644
--- a/src/py/flwr/__init__.py
+++ b/src/py/flwr/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/__init___test.py b/src/py/flwr/__init___test.py
index 161c23f25345..61785d093b2d 100644
--- a/src/py/flwr/__init___test.py
+++ b/src/py/flwr/__init___test.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Adap GmbH. All Rights Reserved.
+# Copyright 2021 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.
diff --git a/src/py/flwr/client/__init__.py b/src/py/flwr/client/__init__.py
index aebc5f28a67e..4ec1082190f2 100644
--- a/src/py/flwr/client/__init__.py
+++ b/src/py/flwr/client/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 7feae478ae0a..b4866bf60ead 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/app_test.py b/src/py/flwr/client/app_test.py
index 1c6e8104f544..6d1df4697a61 100644
--- a/src/py/flwr/client/app_test.py
+++ b/src/py/flwr/client/app_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/client.py b/src/py/flwr/client/client.py
index 8273817d45e8..a0ee3a3e94b8 100644
--- a/src/py/flwr/client/client.py
+++ b/src/py/flwr/client/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/client_test.py b/src/py/flwr/client/client_test.py
index 87a4205bfec4..373c676e5edc 100644
--- a/src/py/flwr/client/client_test.py
+++ b/src/py/flwr/client/client_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/dpfedavg_numpy_client.py b/src/py/flwr/client/dpfedavg_numpy_client.py
index 20b71728bdff..41b4d676df43 100644
--- a/src/py/flwr/client/dpfedavg_numpy_client.py
+++ b/src/py/flwr/client/dpfedavg_numpy_client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/grpc_client/__init__.py b/src/py/flwr/client/grpc_client/__init__.py
index 7925cf6de7c7..9e987e86b254 100644
--- a/src/py/flwr/client/grpc_client/__init__.py
+++ b/src/py/flwr/client/grpc_client/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index cc64ec9a268a..cbef4ef99051 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index 88fe1b4aa0a9..0485fa41db35 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/grpc_rere_client/__init__.py b/src/py/flwr/client/grpc_rere_client/__init__.py
index cdf3fb63ddf4..93903e725776 100644
--- a/src/py/flwr/client/grpc_rere_client/__init__.py
+++ b/src/py/flwr/client/grpc_rere_client/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/grpc_rere_client/connection.py b/src/py/flwr/client/grpc_rere_client/connection.py
index f1a2df233eb1..3dcc147e8eca 100644
--- a/src/py/flwr/client/grpc_rere_client/connection.py
+++ b/src/py/flwr/client/grpc_rere_client/connection.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/message_handler/__init__.py b/src/py/flwr/client/message_handler/__init__.py
index 571bdc403042..653563963de5 100644
--- a/src/py/flwr/client/message_handler/__init__.py
+++ b/src/py/flwr/client/message_handler/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index f50923450f62..b64158ea3a6c 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
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 1fc2269ad75d..d9603101864f 100644
--- a/src/py/flwr/client/message_handler/message_handler_test.py
+++ b/src/py/flwr/client/message_handler/message_handler_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/message_handler/task_handler.py b/src/py/flwr/client/message_handler/task_handler.py
index b48c7433c1da..fc24539998c0 100644
--- a/src/py/flwr/client/message_handler/task_handler.py
+++ b/src/py/flwr/client/message_handler/task_handler.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/message_handler/task_handler_test.py b/src/py/flwr/client/message_handler/task_handler_test.py
index e1b7fac69d24..21f3a2ead98a 100644
--- a/src/py/flwr/client/message_handler/task_handler_test.py
+++ b/src/py/flwr/client/message_handler/task_handler_test.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/numpy_client.py b/src/py/flwr/client/numpy_client.py
index 6c3ae8566266..b535015a9c1f 100644
--- a/src/py/flwr/client/numpy_client.py
+++ b/src/py/flwr/client/numpy_client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/numpy_client_test.py b/src/py/flwr/client/numpy_client_test.py
index 1f4d301ecb4c..526098798e45 100644
--- a/src/py/flwr/client/numpy_client_test.py
+++ b/src/py/flwr/client/numpy_client_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/rest_client/__init__.py b/src/py/flwr/client/rest_client/__init__.py
index 2659915a0be3..c3485483ad35 100644
--- a/src/py/flwr/client/rest_client/__init__.py
+++ b/src/py/flwr/client/rest_client/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/rest_client/connection.py b/src/py/flwr/client/rest_client/connection.py
index 9d3a059bdce8..092e543bf55b 100644
--- a/src/py/flwr/client/rest_client/connection.py
+++ b/src/py/flwr/client/rest_client/connection.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/secure_aggregation/__init__.py b/src/py/flwr/client/secure_aggregation/__init__.py
index 5229d28764f0..37c816a390de 100644
--- a/src/py/flwr/client/secure_aggregation/__init__.py
+++ b/src/py/flwr/client/secure_aggregation/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/secure_aggregation/handler.py b/src/py/flwr/client/secure_aggregation/handler.py
index 16eb9bb6f715..487ed842c93f 100644
--- a/src/py/flwr/client/secure_aggregation/handler.py
+++ b/src/py/flwr/client/secure_aggregation/handler.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/secure_aggregation/secaggplus_handler.py b/src/py/flwr/client/secure_aggregation/secaggplus_handler.py
index d05fd61878a3..efbb00a9d916 100644
--- a/src/py/flwr/client/secure_aggregation/secaggplus_handler.py
+++ b/src/py/flwr/client/secure_aggregation/secaggplus_handler.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/client/secure_aggregation/secaggplus_handler_test.py b/src/py/flwr/client/secure_aggregation/secaggplus_handler_test.py
index 3411f2829028..9693a46af989 100644
--- a/src/py/flwr/client/secure_aggregation/secaggplus_handler_test.py
+++ b/src/py/flwr/client/secure_aggregation/secaggplus_handler_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py
index ec53b13b8398..2f45de45dfc3 100644
--- a/src/py/flwr/common/__init__.py
+++ b/src/py/flwr/common/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/address.py b/src/py/flwr/common/address.py
index d90160633f27..71b6d684597f 100644
--- a/src/py/flwr/common/address.py
+++ b/src/py/flwr/common/address.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/address_test.py b/src/py/flwr/common/address_test.py
index 1a001708df75..c12dd5fd289e 100644
--- a/src/py/flwr/common/address_test.py
+++ b/src/py/flwr/common/address_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/constant.py b/src/py/flwr/common/constant.py
index d27cd2bf287f..49802f2815be 100644
--- a/src/py/flwr/common/constant.py
+++ b/src/py/flwr/common/constant.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/date.py b/src/py/flwr/common/date.py
index f4071b88087d..f47ad5470106 100644
--- a/src/py/flwr/common/date.py
+++ b/src/py/flwr/common/date.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/dp.py b/src/py/flwr/common/dp.py
index 0cb3fadf4fb8..5030ad34805b 100644
--- a/src/py/flwr/common/dp.py
+++ b/src/py/flwr/common/dp.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/grpc.py b/src/py/flwr/common/grpc.py
index 35e13705d4e2..2857048f62a0 100644
--- a/src/py/flwr/common/grpc.py
+++ b/src/py/flwr/common/grpc.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/logger.py b/src/py/flwr/common/logger.py
index 1c2192deb392..e543d6565878 100644
--- a/src/py/flwr/common/logger.py
+++ b/src/py/flwr/common/logger.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/parameter.py b/src/py/flwr/common/parameter.py
index f77d30f58b0d..eefd7abc19a8 100644
--- a/src/py/flwr/common/parameter.py
+++ b/src/py/flwr/common/parameter.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/parameter_test.py b/src/py/flwr/common/parameter_test.py
index a20d6cbb5b54..965038ead6d2 100644
--- a/src/py/flwr/common/parameter_test.py
+++ b/src/py/flwr/common/parameter_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/secure_aggregation/__init__.py b/src/py/flwr/common/secure_aggregation/__init__.py
index 51851528a47e..b4e0acc0c148 100644
--- a/src/py/flwr/common/secure_aggregation/__init__.py
+++ b/src/py/flwr/common/secure_aggregation/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/secure_aggregation/crypto/__init__.py b/src/py/flwr/common/secure_aggregation/crypto/__init__.py
index 4d7dc1943f00..2cb34493f7d0 100644
--- a/src/py/flwr/common/secure_aggregation/crypto/__init__.py
+++ b/src/py/flwr/common/secure_aggregation/crypto/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/secure_aggregation/crypto/shamir.py b/src/py/flwr/common/secure_aggregation/crypto/shamir.py
index 2f2821287b44..e56e21b89371 100644
--- a/src/py/flwr/common/secure_aggregation/crypto/shamir.py
+++ b/src/py/flwr/common/secure_aggregation/crypto/shamir.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/secure_aggregation/crypto/symmetric_encryption.py b/src/py/flwr/common/secure_aggregation/crypto/symmetric_encryption.py
index 5bcb7ec12255..844a93f3bde9 100644
--- a/src/py/flwr/common/secure_aggregation/crypto/symmetric_encryption.py
+++ b/src/py/flwr/common/secure_aggregation/crypto/symmetric_encryption.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py b/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py
index 1b7266268881..57afa56b7a08 100644
--- a/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py
+++ b/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/secure_aggregation/quantization.py b/src/py/flwr/common/secure_aggregation/quantization.py
index f1abfdbbdb9b..56c25e2bd59c 100644
--- a/src/py/flwr/common/secure_aggregation/quantization.py
+++ b/src/py/flwr/common/secure_aggregation/quantization.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/secure_aggregation/secaggplus_constants.py b/src/py/flwr/common/secure_aggregation/secaggplus_constants.py
index 41220d6149a9..8dd21a6016f1 100644
--- a/src/py/flwr/common/secure_aggregation/secaggplus_constants.py
+++ b/src/py/flwr/common/secure_aggregation/secaggplus_constants.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/secure_aggregation/secaggplus_utils.py b/src/py/flwr/common/secure_aggregation/secaggplus_utils.py
index 8293b0c08571..def677e9d5d9 100644
--- a/src/py/flwr/common/secure_aggregation/secaggplus_utils.py
+++ b/src/py/flwr/common/secure_aggregation/secaggplus_utils.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/serde.py b/src/py/flwr/common/serde.py
index 04628556e826..c8c73e87e04a 100644
--- a/src/py/flwr/common/serde.py
+++ b/src/py/flwr/common/serde.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py
index f537cf071354..ba07890f4658 100644
--- a/src/py/flwr/common/serde_test.py
+++ b/src/py/flwr/common/serde_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/telemetry.py b/src/py/flwr/common/telemetry.py
index 9755a974ce21..d56726d83378 100644
--- a/src/py/flwr/common/telemetry.py
+++ b/src/py/flwr/common/telemetry.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/telemetry_test.py b/src/py/flwr/common/telemetry_test.py
index b22a0fffb150..006f4422bc1d 100644
--- a/src/py/flwr/common/telemetry_test.py
+++ b/src/py/flwr/common/telemetry_test.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/common/typing.py b/src/py/flwr/common/typing.py
index 4257bf8e3279..6c0266f5eec8 100644
--- a/src/py/flwr/common/typing.py
+++ b/src/py/flwr/common/typing.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/driver/__init__.py b/src/py/flwr/driver/__init__.py
index 5295b484afc8..8c100e935f70 100644
--- a/src/py/flwr/driver/__init__.py
+++ b/src/py/flwr/driver/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/driver/app.py b/src/py/flwr/driver/app.py
index f45104aa359d..eeacfc3d9ede 100644
--- a/src/py/flwr/driver/app.py
+++ b/src/py/flwr/driver/app.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/driver/app_test.py b/src/py/flwr/driver/app_test.py
index 4fcd924f8432..91b4fd30bc4b 100644
--- a/src/py/flwr/driver/app_test.py
+++ b/src/py/flwr/driver/app_test.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/driver/driver.py b/src/py/flwr/driver/driver.py
index 130cd2bbc707..8e029c1e1be1 100644
--- a/src/py/flwr/driver/driver.py
+++ b/src/py/flwr/driver/driver.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/driver/driver_client_proxy.py b/src/py/flwr/driver/driver_client_proxy.py
index deb472458a15..b732cf66c220 100644
--- a/src/py/flwr/driver/driver_client_proxy.py
+++ b/src/py/flwr/driver/driver_client_proxy.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/driver/driver_client_proxy_test.py b/src/py/flwr/driver/driver_client_proxy_test.py
index f413b8d8d99d..82b5b46d7810 100644
--- a/src/py/flwr/driver/driver_client_proxy_test.py
+++ b/src/py/flwr/driver/driver_client_proxy_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/driver/driver_test.py b/src/py/flwr/driver/driver_test.py
index 070d3219a5b4..ef2a17e8538d 100644
--- a/src/py/flwr/driver/driver_test.py
+++ b/src/py/flwr/driver/driver_test.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/proto/__init__.py b/src/py/flwr/proto/__init__.py
index a87c9e62f22a..b903538194ec 100644
--- a/src/py/flwr/proto/__init__.py
+++ b/src/py/flwr/proto/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/__init__.py b/src/py/flwr/server/__init__.py
index 7e97fcd25921..74abe8dd463c 100644
--- a/src/py/flwr/server/__init__.py
+++ b/src/py/flwr/server/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index 781791df7535..b8ad26585a1b 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/client_manager.py b/src/py/flwr/server/client_manager.py
index ccadf33daff4..7956e282bd2c 100644
--- a/src/py/flwr/server/client_manager.py
+++ b/src/py/flwr/server/client_manager.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/client_manager_test.py b/src/py/flwr/server/client_manager_test.py
index 7674cc550864..8145b9b2ab7f 100644
--- a/src/py/flwr/server/client_manager_test.py
+++ b/src/py/flwr/server/client_manager_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/client_proxy.py b/src/py/flwr/server/client_proxy.py
index 2ea015064f41..7d0547be304b 100644
--- a/src/py/flwr/server/client_proxy.py
+++ b/src/py/flwr/server/client_proxy.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/client_proxy_test.py b/src/py/flwr/server/client_proxy_test.py
index 7cb3e57150e7..266e4cbeb266 100644
--- a/src/py/flwr/server/client_proxy_test.py
+++ b/src/py/flwr/server/client_proxy_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/criterion.py b/src/py/flwr/server/criterion.py
index 6c01f88862c5..199a609104cb 100644
--- a/src/py/flwr/server/criterion.py
+++ b/src/py/flwr/server/criterion.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/criterion_test.py b/src/py/flwr/server/criterion_test.py
index 26c70db3275d..a7e5b62b5977 100644
--- a/src/py/flwr/server/criterion_test.py
+++ b/src/py/flwr/server/criterion_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/driver/__init__.py b/src/py/flwr/server/driver/__init__.py
index 508a7e63eaff..2bfe63e6065f 100644
--- a/src/py/flwr/server/driver/__init__.py
+++ b/src/py/flwr/server/driver/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/server/driver/driver_servicer.py b/src/py/flwr/server/driver/driver_servicer.py
index 87f871070b4a..f96b3b1262ac 100644
--- a/src/py/flwr/server/driver/driver_servicer.py
+++ b/src/py/flwr/server/driver/driver_servicer.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/driver/driver_servicer_test.py b/src/py/flwr/server/driver/driver_servicer_test.py
index 5e71a36b12e2..c432c026a632 100644
--- a/src/py/flwr/server/driver/driver_servicer_test.py
+++ b/src/py/flwr/server/driver/driver_servicer_test.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/server/fleet/__init__.py b/src/py/flwr/server/fleet/__init__.py
index 6b06784f0ddc..d3c3ef90163d 100644
--- a/src/py/flwr/server/fleet/__init__.py
+++ b/src/py/flwr/server/fleet/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/__init__.py b/src/py/flwr/server/fleet/grpc_bidi/__init__.py
index c6681f4ce675..bae8bc431edd 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/__init__.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py b/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py
index 88265b0e1123..dc94bf3912d7 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer.py b/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer.py
index 96fc88944bfe..1f7a8e9259fc 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer_test.py b/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer_test.py
index 51f08e341962..64140ed274c9 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer_test.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge.py b/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge.py
index 9e4012e90700..6ae38ea3d805 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge_test.py b/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge_test.py
index a9f7bceed85e..18a2144072ed 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge_test.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy.py b/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy.py
index 4ed4e7da197a..b9bc7330db31 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy_test.py b/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy_test.py
index b8816c34617a..329f29b3f616 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy_test.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_server.py b/src/py/flwr/server/fleet/grpc_bidi/grpc_server.py
index cb05bb8569a6..fc81e8eb8f4c 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_server.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/grpc_server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_server_test.py b/src/py/flwr/server/fleet/grpc_bidi/grpc_server_test.py
index 503896e24a5c..4cd093d6ab0f 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_server_test.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/grpc_server_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_bidi/ins_scheduler.py b/src/py/flwr/server/fleet/grpc_bidi/ins_scheduler.py
index 9506c6f0ad5d..1c737d31c7fc 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/ins_scheduler.py
+++ b/src/py/flwr/server/fleet/grpc_bidi/ins_scheduler.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_rere/__init__.py b/src/py/flwr/server/fleet/grpc_rere/__init__.py
index 03fe4b037d2d..61ab71d91400 100644
--- a/src/py/flwr/server/fleet/grpc_rere/__init__.py
+++ b/src/py/flwr/server/fleet/grpc_rere/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/grpc_rere/fleet_servicer.py b/src/py/flwr/server/fleet/grpc_rere/fleet_servicer.py
index 69a0ce2c7243..022470cffe8a 100644
--- a/src/py/flwr/server/fleet/grpc_rere/fleet_servicer.py
+++ b/src/py/flwr/server/fleet/grpc_rere/fleet_servicer.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/message_handler/__init__.py b/src/py/flwr/server/fleet/message_handler/__init__.py
index 93484bae3428..18b0f11fa6c5 100644
--- a/src/py/flwr/server/fleet/message_handler/__init__.py
+++ b/src/py/flwr/server/fleet/message_handler/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/message_handler/message_handler.py b/src/py/flwr/server/fleet/message_handler/message_handler.py
index da27976f112d..71876386f059 100644
--- a/src/py/flwr/server/fleet/message_handler/message_handler.py
+++ b/src/py/flwr/server/fleet/message_handler/message_handler.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/message_handler/message_handler_test.py b/src/py/flwr/server/fleet/message_handler/message_handler_test.py
index e5d889adeb3f..25fd822492f2 100644
--- a/src/py/flwr/server/fleet/message_handler/message_handler_test.py
+++ b/src/py/flwr/server/fleet/message_handler/message_handler_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/rest_rere/__init__.py b/src/py/flwr/server/fleet/rest_rere/__init__.py
index 45ded4d9ea33..a926f9ca0bfc 100644
--- a/src/py/flwr/server/fleet/rest_rere/__init__.py
+++ b/src/py/flwr/server/fleet/rest_rere/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/fleet/rest_rere/rest_api.py b/src/py/flwr/server/fleet/rest_rere/rest_api.py
index cb9ac7b2ebc5..cd1e47f24f00 100644
--- a/src/py/flwr/server/fleet/rest_rere/rest_api.py
+++ b/src/py/flwr/server/fleet/rest_rere/rest_api.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/history.py b/src/py/flwr/server/history.py
index 6dc4546e4445..ad5f5d0fc870 100644
--- a/src/py/flwr/server/history.py
+++ b/src/py/flwr/server/history.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/history_test.py b/src/py/flwr/server/history_test.py
index 338b06cc89e6..adb9d697e409 100644
--- a/src/py/flwr/server/history_test.py
+++ b/src/py/flwr/server/history_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/server.py b/src/py/flwr/server/server.py
index 279914e801d6..cf3a4d9aa07c 100644
--- a/src/py/flwr/server/server.py
+++ b/src/py/flwr/server/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/server_test.py b/src/py/flwr/server/server_test.py
index 00dabd16d8d2..63ec1021ff5c 100644
--- a/src/py/flwr/server/server_test.py
+++ b/src/py/flwr/server/server_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/state/__init__.py b/src/py/flwr/server/state/__init__.py
index eb5185d95038..7f260d733bbe 100644
--- a/src/py/flwr/server/state/__init__.py
+++ b/src/py/flwr/server/state/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/state/in_memory_state.py b/src/py/flwr/server/state/in_memory_state.py
index 85515809bd4e..384839b7461f 100644
--- a/src/py/flwr/server/state/in_memory_state.py
+++ b/src/py/flwr/server/state/in_memory_state.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/state/sqlite_state.py b/src/py/flwr/server/state/sqlite_state.py
index 2f878443ccd1..7bc323f17929 100644
--- a/src/py/flwr/server/state/sqlite_state.py
+++ b/src/py/flwr/server/state/sqlite_state.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/state/sqlite_state_test.py b/src/py/flwr/server/state/sqlite_state_test.py
index b9c0df9ed134..da8fead1438e 100644
--- a/src/py/flwr/server/state/sqlite_state_test.py
+++ b/src/py/flwr/server/state/sqlite_state_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/state/state.py b/src/py/flwr/server/state/state.py
index 1e08d9e4f5b7..fd8bbc8e8e25 100644
--- a/src/py/flwr/server/state/state.py
+++ b/src/py/flwr/server/state/state.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/server/state/state_factory.py b/src/py/flwr/server/state/state_factory.py
index c980b6ce6761..62a00d910828 100644
--- a/src/py/flwr/server/state/state_factory.py
+++ b/src/py/flwr/server/state/state_factory.py
@@ -1,4 +1,4 @@
-# Copyright 2022 Adap GmbH. All Rights Reserved.
+# Copyright 2022 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.
diff --git a/src/py/flwr/server/state/state_test.py b/src/py/flwr/server/state/state_test.py
index 11d6d2386a4e..59299451c3d8 100644
--- a/src/py/flwr/server/state/state_test.py
+++ b/src/py/flwr/server/state/state_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/__init__.py b/src/py/flwr/server/strategy/__init__.py
index f14fd0540893..72429694bfe7 100644
--- a/src/py/flwr/server/strategy/__init__.py
+++ b/src/py/flwr/server/strategy/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/aggregate.py b/src/py/flwr/server/strategy/aggregate.py
index c80da3ff37a2..42390a08a110 100644
--- a/src/py/flwr/server/strategy/aggregate.py
+++ b/src/py/flwr/server/strategy/aggregate.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/aggregate_test.py b/src/py/flwr/server/strategy/aggregate_test.py
index 6e6c2578eb35..81cc7189b14d 100644
--- a/src/py/flwr/server/strategy/aggregate_test.py
+++ b/src/py/flwr/server/strategy/aggregate_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/dpfedavg_adaptive.py b/src/py/flwr/server/strategy/dpfedavg_adaptive.py
index f331bca45c53..3269735e9d73 100644
--- a/src/py/flwr/server/strategy/dpfedavg_adaptive.py
+++ b/src/py/flwr/server/strategy/dpfedavg_adaptive.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/dpfedavg_fixed.py b/src/py/flwr/server/strategy/dpfedavg_fixed.py
index 43dc2249736e..0154cfd79fc5 100644
--- a/src/py/flwr/server/strategy/dpfedavg_fixed.py
+++ b/src/py/flwr/server/strategy/dpfedavg_fixed.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fault_tolerant_fedavg.py b/src/py/flwr/server/strategy/fault_tolerant_fedavg.py
index 9bd6e2d24d2e..663ac8872c39 100644
--- a/src/py/flwr/server/strategy/fault_tolerant_fedavg.py
+++ b/src/py/flwr/server/strategy/fault_tolerant_fedavg.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fault_tolerant_fedavg_test.py b/src/py/flwr/server/strategy/fault_tolerant_fedavg_test.py
index 92dd05179579..98f4cac032cb 100644
--- a/src/py/flwr/server/strategy/fault_tolerant_fedavg_test.py
+++ b/src/py/flwr/server/strategy/fault_tolerant_fedavg_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedadagrad.py b/src/py/flwr/server/strategy/fedadagrad.py
index 06ec06c7fa59..085362891d94 100644
--- a/src/py/flwr/server/strategy/fedadagrad.py
+++ b/src/py/flwr/server/strategy/fedadagrad.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedadagrad_test.py b/src/py/flwr/server/strategy/fedadagrad_test.py
index 6a47ca39b062..b3380a5be2f9 100644
--- a/src/py/flwr/server/strategy/fedadagrad_test.py
+++ b/src/py/flwr/server/strategy/fedadagrad_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedadam.py b/src/py/flwr/server/strategy/fedadam.py
index afdeb9a55984..ca6229029376 100644
--- a/src/py/flwr/server/strategy/fedadam.py
+++ b/src/py/flwr/server/strategy/fedadam.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedavg.py b/src/py/flwr/server/strategy/fedavg.py
index 2e8fb41b4acd..86afd31e2bfb 100644
--- a/src/py/flwr/server/strategy/fedavg.py
+++ b/src/py/flwr/server/strategy/fedavg.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedavg_android.py b/src/py/flwr/server/strategy/fedavg_android.py
index c810dc65ad1b..377397678e38 100644
--- a/src/py/flwr/server/strategy/fedavg_android.py
+++ b/src/py/flwr/server/strategy/fedavg_android.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedavg_test.py b/src/py/flwr/server/strategy/fedavg_test.py
index dd7fdf0ab157..947736f4a571 100644
--- a/src/py/flwr/server/strategy/fedavg_test.py
+++ b/src/py/flwr/server/strategy/fedavg_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedavgm.py b/src/py/flwr/server/strategy/fedavgm.py
index 3023c20bce64..37cccd01479c 100644
--- a/src/py/flwr/server/strategy/fedavgm.py
+++ b/src/py/flwr/server/strategy/fedavgm.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedavgm_test.py b/src/py/flwr/server/strategy/fedavgm_test.py
index dad76fd1d055..a0e942171627 100644
--- a/src/py/flwr/server/strategy/fedavgm_test.py
+++ b/src/py/flwr/server/strategy/fedavgm_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedmedian.py b/src/py/flwr/server/strategy/fedmedian.py
index 28b57a1ab257..7a5bf1425b44 100644
--- a/src/py/flwr/server/strategy/fedmedian.py
+++ b/src/py/flwr/server/strategy/fedmedian.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedmedian_test.py b/src/py/flwr/server/strategy/fedmedian_test.py
index 1e494ee91392..180503df6c80 100644
--- a/src/py/flwr/server/strategy/fedmedian_test.py
+++ b/src/py/flwr/server/strategy/fedmedian_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedopt.py b/src/py/flwr/server/strategy/fedopt.py
index 23f2a8bd2d50..78dd92061c07 100644
--- a/src/py/flwr/server/strategy/fedopt.py
+++ b/src/py/flwr/server/strategy/fedopt.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedprox.py b/src/py/flwr/server/strategy/fedprox.py
index 2ef1b6d60a29..b2e3db7c31f4 100644
--- a/src/py/flwr/server/strategy/fedprox.py
+++ b/src/py/flwr/server/strategy/fedprox.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedxgb_nn_avg.py b/src/py/flwr/server/strategy/fedxgb_nn_avg.py
index 0f6585ed09df..020e0ef71267 100644
--- a/src/py/flwr/server/strategy/fedxgb_nn_avg.py
+++ b/src/py/flwr/server/strategy/fedxgb_nn_avg.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/fedyogi.py b/src/py/flwr/server/strategy/fedyogi.py
index 19afae1d599f..245090534c7c 100644
--- a/src/py/flwr/server/strategy/fedyogi.py
+++ b/src/py/flwr/server/strategy/fedyogi.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/krum.py b/src/py/flwr/server/strategy/krum.py
index fecc789680c4..d7f15531902f 100644
--- a/src/py/flwr/server/strategy/krum.py
+++ b/src/py/flwr/server/strategy/krum.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/krum_test.py b/src/py/flwr/server/strategy/krum_test.py
index d97045bfc76f..81e59230739a 100644
--- a/src/py/flwr/server/strategy/krum_test.py
+++ b/src/py/flwr/server/strategy/krum_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/multikrum_test.py b/src/py/flwr/server/strategy/multikrum_test.py
index bc5eaf7615cb..1469db104252 100644
--- a/src/py/flwr/server/strategy/multikrum_test.py
+++ b/src/py/flwr/server/strategy/multikrum_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/qfedavg.py b/src/py/flwr/server/strategy/qfedavg.py
index 7946ab579d84..94a67fbcbfae 100644
--- a/src/py/flwr/server/strategy/qfedavg.py
+++ b/src/py/flwr/server/strategy/qfedavg.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/strategy/strategy.py b/src/py/flwr/server/strategy/strategy.py
index a2c774fe2506..cfdfe2e246c5 100644
--- a/src/py/flwr/server/strategy/strategy.py
+++ b/src/py/flwr/server/strategy/strategy.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/utils/__init__.py b/src/py/flwr/server/utils/__init__.py
index d55e3dbf1fe9..c370716adaac 100644
--- a/src/py/flwr/server/utils/__init__.py
+++ b/src/py/flwr/server/utils/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/utils/tensorboard.py b/src/py/flwr/server/utils/tensorboard.py
index e69e31079b55..3e8d1e62411e 100644
--- a/src/py/flwr/server/utils/tensorboard.py
+++ b/src/py/flwr/server/utils/tensorboard.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/utils/tensorboard_test.py b/src/py/flwr/server/utils/tensorboard_test.py
index bf2526f47220..1827a42cf6e6 100644
--- a/src/py/flwr/server/utils/tensorboard_test.py
+++ b/src/py/flwr/server/utils/tensorboard_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/utils/validator.py b/src/py/flwr/server/utils/validator.py
index f83a8245edb7..fd89a01e4a4e 100644
--- a/src/py/flwr/server/utils/validator.py
+++ b/src/py/flwr/server/utils/validator.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/server/utils/validator_test.py b/src/py/flwr/server/utils/validator_test.py
index 54840731048f..cab51fbf46de 100644
--- a/src/py/flwr/server/utils/validator_test.py
+++ b/src/py/flwr/server/utils/validator_test.py
@@ -1,4 +1,4 @@
-# Copyright 2023 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/simulation/__init__.py b/src/py/flwr/simulation/__init__.py
index d8effdf40e1b..724ea9273916 100644
--- a/src/py/flwr/simulation/__init__.py
+++ b/src/py/flwr/simulation/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py
index 5c7a3e7423a3..bfa72debaae4 100644
--- a/src/py/flwr/simulation/app.py
+++ b/src/py/flwr/simulation/app.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr/simulation/ray_transport/__init__.py b/src/py/flwr/simulation/ray_transport/__init__.py
index ac1668fa1166..0e82b75bb4b3 100644
--- a/src/py/flwr/simulation/ray_transport/__init__.py
+++ b/src/py/flwr/simulation/ray_transport/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
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 482ae314aadd..0365cce073b4 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/__init__.py b/src/py/flwr_example/__init__.py
index 5bd3efe97198..cd2e721e36b5 100644
--- a/src/py/flwr_example/__init__.py
+++ b/src/py/flwr_example/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_cifar/__init__.py b/src/py/flwr_example/pytorch_cifar/__init__.py
index 1304ca888f47..e1a6d4c2e25e 100644
--- a/src/py/flwr_example/pytorch_cifar/__init__.py
+++ b/src/py/flwr_example/pytorch_cifar/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_cifar/cifar.py b/src/py/flwr_example/pytorch_cifar/cifar.py
index 6c83ba041ba8..279e635de356 100644
--- a/src/py/flwr_example/pytorch_cifar/cifar.py
+++ b/src/py/flwr_example/pytorch_cifar/cifar.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_cifar/cifar_test.py b/src/py/flwr_example/pytorch_cifar/cifar_test.py
index 12b0f047fd39..e9f908531020 100644
--- a/src/py/flwr_example/pytorch_cifar/cifar_test.py
+++ b/src/py/flwr_example/pytorch_cifar/cifar_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_cifar/client.py b/src/py/flwr_example/pytorch_cifar/client.py
index 18a4bf93f36a..369e6a84377d 100644
--- a/src/py/flwr_example/pytorch_cifar/client.py
+++ b/src/py/flwr_example/pytorch_cifar/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_cifar/run-clients.sh b/src/py/flwr_example/pytorch_cifar/run-clients.sh
index 7d3fb2ee0e74..de2c6e0d0cb4 100755
--- a/src/py/flwr_example/pytorch_cifar/run-clients.sh
+++ b/src/py/flwr_example/pytorch_cifar/run-clients.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_cifar/run-server.sh b/src/py/flwr_example/pytorch_cifar/run-server.sh
index d355919b4ff1..6bb0d6148bc3 100755
--- a/src/py/flwr_example/pytorch_cifar/run-server.sh
+++ b/src/py/flwr_example/pytorch_cifar/run-server.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_cifar/server.py b/src/py/flwr_example/pytorch_cifar/server.py
index 84348ee92595..5acb4a114b44 100644
--- a/src/py/flwr_example/pytorch_cifar/server.py
+++ b/src/py/flwr_example/pytorch_cifar/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_imagenet/__init__.py b/src/py/flwr_example/pytorch_imagenet/__init__.py
index 17c58ac20cbd..5a78f1f627df 100644
--- a/src/py/flwr_example/pytorch_imagenet/__init__.py
+++ b/src/py/flwr_example/pytorch_imagenet/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_imagenet/client.py b/src/py/flwr_example/pytorch_imagenet/client.py
index 6a878b3f9d88..72732edee6ef 100644
--- a/src/py/flwr_example/pytorch_imagenet/client.py
+++ b/src/py/flwr_example/pytorch_imagenet/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_imagenet/imagenet.py b/src/py/flwr_example/pytorch_imagenet/imagenet.py
index 1d55bc1368fd..20ec24fd85bf 100644
--- a/src/py/flwr_example/pytorch_imagenet/imagenet.py
+++ b/src/py/flwr_example/pytorch_imagenet/imagenet.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_imagenet/run-clients.sh b/src/py/flwr_example/pytorch_imagenet/run-clients.sh
index 13b689743816..f907ac67db12 100755
--- a/src/py/flwr_example/pytorch_imagenet/run-clients.sh
+++ b/src/py/flwr_example/pytorch_imagenet/run-clients.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_imagenet/run-server.sh b/src/py/flwr_example/pytorch_imagenet/run-server.sh
index ea2e28b683ce..cd6909202d6f 100755
--- a/src/py/flwr_example/pytorch_imagenet/run-server.sh
+++ b/src/py/flwr_example/pytorch_imagenet/run-server.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_imagenet/server.py b/src/py/flwr_example/pytorch_imagenet/server.py
index cb640b1d4275..cde8a3d572fd 100644
--- a/src/py/flwr_example/pytorch_imagenet/server.py
+++ b/src/py/flwr_example/pytorch_imagenet/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_save_weights/__init__.py b/src/py/flwr_example/pytorch_save_weights/__init__.py
index 1fbf4e2fe44e..7a4638952123 100644
--- a/src/py/flwr_example/pytorch_save_weights/__init__.py
+++ b/src/py/flwr_example/pytorch_save_weights/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_save_weights/cifar.py b/src/py/flwr_example/pytorch_save_weights/cifar.py
index e7731e88ea09..e9fb0552a054 100644
--- a/src/py/flwr_example/pytorch_save_weights/cifar.py
+++ b/src/py/flwr_example/pytorch_save_weights/cifar.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_save_weights/client.py b/src/py/flwr_example/pytorch_save_weights/client.py
index bd1b53f1ebf4..6f35fdb881c3 100644
--- a/src/py/flwr_example/pytorch_save_weights/client.py
+++ b/src/py/flwr_example/pytorch_save_weights/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_save_weights/run-clients.sh b/src/py/flwr_example/pytorch_save_weights/run-clients.sh
index 5d4e09f37129..9065415148a0 100755
--- a/src/py/flwr_example/pytorch_save_weights/run-clients.sh
+++ b/src/py/flwr_example/pytorch_save_weights/run-clients.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_save_weights/run-server.sh b/src/py/flwr_example/pytorch_save_weights/run-server.sh
index c8464e204316..0464c371169c 100755
--- a/src/py/flwr_example/pytorch_save_weights/run-server.sh
+++ b/src/py/flwr_example/pytorch_save_weights/run-server.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/pytorch_save_weights/server.py b/src/py/flwr_example/pytorch_save_weights/server.py
index d6672d7a71c7..ede93e268c17 100644
--- a/src/py/flwr_example/pytorch_save_weights/server.py
+++ b/src/py/flwr_example/pytorch_save_weights/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/quickstart_pytorch/client.py b/src/py/flwr_example/quickstart_pytorch/client.py
index 678e95269e0d..fc675f123cc8 100644
--- a/src/py/flwr_example/quickstart_pytorch/client.py
+++ b/src/py/flwr_example/quickstart_pytorch/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/quickstart_pytorch/mnist.py b/src/py/flwr_example/quickstart_pytorch/mnist.py
index ccb04a9a35dd..9a65145e6ca3 100644
--- a/src/py/flwr_example/quickstart_pytorch/mnist.py
+++ b/src/py/flwr_example/quickstart_pytorch/mnist.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/quickstart_pytorch/run-clients.sh b/src/py/flwr_example/quickstart_pytorch/run-clients.sh
index 699a1f12a8ce..f53d63dc6168 100755
--- a/src/py/flwr_example/quickstart_pytorch/run-clients.sh
+++ b/src/py/flwr_example/quickstart_pytorch/run-clients.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/quickstart_pytorch/run-server.sh b/src/py/flwr_example/quickstart_pytorch/run-server.sh
index e3da22525478..29cb8cf7caf7 100755
--- a/src/py/flwr_example/quickstart_pytorch/run-server.sh
+++ b/src/py/flwr_example/quickstart_pytorch/run-server.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/quickstart_pytorch/server.py b/src/py/flwr_example/quickstart_pytorch/server.py
index 52a10eb53d2c..b8cc1e15aca1 100644
--- a/src/py/flwr_example/quickstart_pytorch/server.py
+++ b/src/py/flwr_example/quickstart_pytorch/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/quickstart_tensorflow/run-clients.sh b/src/py/flwr_example/quickstart_tensorflow/run-clients.sh
index 7323a46c248e..5747bfe5fe1b 100755
--- a/src/py/flwr_example/quickstart_tensorflow/run-clients.sh
+++ b/src/py/flwr_example/quickstart_tensorflow/run-clients.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/quickstart_tensorflow/run-server.sh b/src/py/flwr_example/quickstart_tensorflow/run-server.sh
index 0262da90fcde..030167972ac0 100755
--- a/src/py/flwr_example/quickstart_tensorflow/run-server.sh
+++ b/src/py/flwr_example/quickstart_tensorflow/run-server.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/__init__.py b/src/py/flwr_example/tensorflow_fashion_mnist/__init__.py
index 035cd6fb6a5d..3370fa33e781 100644
--- a/src/py/flwr_example/tensorflow_fashion_mnist/__init__.py
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/client.py b/src/py/flwr_example/tensorflow_fashion_mnist/client.py
index 4688a064af8c..bcee031163e1 100644
--- a/src/py/flwr_example/tensorflow_fashion_mnist/client.py
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/download.py b/src/py/flwr_example/tensorflow_fashion_mnist/download.py
index 385590c57699..c2f0eb580cf0 100644
--- a/src/py/flwr_example/tensorflow_fashion_mnist/download.py
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/download.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist.py b/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist.py
index 6187decddf22..6c7ad2eead79 100644
--- a/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist.py
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py b/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py
index bd9b41fb9d31..1213410fbc34 100644
--- a/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh b/src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh
index a34232ec699d..732688b9be08 100755
--- a/src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh b/src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh
index f47446f54595..d80f3c8b30ce 100755
--- a/src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/server.py b/src/py/flwr_example/tensorflow_fashion_mnist/server.py
index 493f56c5fa9d..f16da1e4bc93 100644
--- a/src/py/flwr_example/tensorflow_fashion_mnist/server.py
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/__init__.py b/src/py/flwr_experimental/__init__.py
index b614793e9309..1e9952588480 100644
--- a/src/py/flwr_experimental/__init__.py
+++ b/src/py/flwr_experimental/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/__init__.py b/src/py/flwr_experimental/baseline/__init__.py
index 9033f2402f9e..b2fefc3f319d 100644
--- a/src/py/flwr_experimental/baseline/__init__.py
+++ b/src/py/flwr_experimental/baseline/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/command.py b/src/py/flwr_experimental/baseline/command.py
index 2006fedd7a94..a776347bacf6 100644
--- a/src/py/flwr_experimental/baseline/command.py
+++ b/src/py/flwr_experimental/baseline/command.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/common/__init__.py b/src/py/flwr_experimental/baseline/common/__init__.py
index 462e85995fc9..706d2c41cb69 100644
--- a/src/py/flwr_experimental/baseline/common/__init__.py
+++ b/src/py/flwr_experimental/baseline/common/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/common/client.py b/src/py/flwr_experimental/baseline/common/client.py
index f74f3418fae7..18346218d919 100644
--- a/src/py/flwr_experimental/baseline/common/client.py
+++ b/src/py/flwr_experimental/baseline/common/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/common/common.py b/src/py/flwr_experimental/baseline/common/common.py
index fea900105a13..7be848f96d79 100644
--- a/src/py/flwr_experimental/baseline/common/common.py
+++ b/src/py/flwr_experimental/baseline/common/common.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/common/data.py b/src/py/flwr_experimental/baseline/common/data.py
index fad1b7d8cc92..7d939d608310 100644
--- a/src/py/flwr_experimental/baseline/common/data.py
+++ b/src/py/flwr_experimental/baseline/common/data.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/config/__init__.py b/src/py/flwr_experimental/baseline/config/__init__.py
index c78df8f9a344..0221a92188e5 100644
--- a/src/py/flwr_experimental/baseline/config/__init__.py
+++ b/src/py/flwr_experimental/baseline/config/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/config/config.py b/src/py/flwr_experimental/baseline/config/config.py
index 995e515bf1b9..5170ea7f7e26 100644
--- a/src/py/flwr_experimental/baseline/config/config.py
+++ b/src/py/flwr_experimental/baseline/config/config.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/config/config_test.py b/src/py/flwr_experimental/baseline/config/config_test.py
index 244d15b74cf6..1e8377b3be1e 100644
--- a/src/py/flwr_experimental/baseline/config/config_test.py
+++ b/src/py/flwr_experimental/baseline/config/config_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/dataset/__init__.py b/src/py/flwr_experimental/baseline/dataset/__init__.py
index 0eaab456e4fb..aa19c2fbfce8 100644
--- a/src/py/flwr_experimental/baseline/dataset/__init__.py
+++ b/src/py/flwr_experimental/baseline/dataset/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/dataset/dataset.py b/src/py/flwr_experimental/baseline/dataset/dataset.py
index 789f3e49531e..8e27ad71821d 100644
--- a/src/py/flwr_experimental/baseline/dataset/dataset.py
+++ b/src/py/flwr_experimental/baseline/dataset/dataset.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/dataset/dataset_test.py b/src/py/flwr_experimental/baseline/dataset/dataset_test.py
index 565f42a494b4..4c484b8836bc 100644
--- a/src/py/flwr_experimental/baseline/dataset/dataset_test.py
+++ b/src/py/flwr_experimental/baseline/dataset/dataset_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/dataset/tf_cifar_partitioned.py b/src/py/flwr_experimental/baseline/dataset/tf_cifar_partitioned.py
index 4fb1ec0ff0f8..61b6e31c29ab 100644
--- a/src/py/flwr_experimental/baseline/dataset/tf_cifar_partitioned.py
+++ b/src/py/flwr_experimental/baseline/dataset/tf_cifar_partitioned.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/dataset/tf_cifar_partitioned_test.py b/src/py/flwr_experimental/baseline/dataset/tf_cifar_partitioned_test.py
index ddf0016688f6..dc655682adec 100644
--- a/src/py/flwr_experimental/baseline/dataset/tf_cifar_partitioned_test.py
+++ b/src/py/flwr_experimental/baseline/dataset/tf_cifar_partitioned_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/dataset/tf_fashion_mnist_partitioned.py b/src/py/flwr_experimental/baseline/dataset/tf_fashion_mnist_partitioned.py
index 86c71d6437ea..da7d70434bb4 100644
--- a/src/py/flwr_experimental/baseline/dataset/tf_fashion_mnist_partitioned.py
+++ b/src/py/flwr_experimental/baseline/dataset/tf_fashion_mnist_partitioned.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/dataset/tf_fashion_mnist_partitioned_test.py b/src/py/flwr_experimental/baseline/dataset/tf_fashion_mnist_partitioned_test.py
index 5ede8ba54f88..2782bcf0353a 100644
--- a/src/py/flwr_experimental/baseline/dataset/tf_fashion_mnist_partitioned_test.py
+++ b/src/py/flwr_experimental/baseline/dataset/tf_fashion_mnist_partitioned_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/dataset/tf_hotkey_partitioned.py b/src/py/flwr_experimental/baseline/dataset/tf_hotkey_partitioned.py
index 0a3279432f65..265d67301b64 100644
--- a/src/py/flwr_experimental/baseline/dataset/tf_hotkey_partitioned.py
+++ b/src/py/flwr_experimental/baseline/dataset/tf_hotkey_partitioned.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/ip.py b/src/py/flwr_experimental/baseline/ip.py
index db800d92dd2a..25efefa40d0a 100644
--- a/src/py/flwr_experimental/baseline/ip.py
+++ b/src/py/flwr_experimental/baseline/ip.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/model/__init__.py b/src/py/flwr_experimental/baseline/model/__init__.py
index 21bebe00002b..70c22a137002 100644
--- a/src/py/flwr_experimental/baseline/model/__init__.py
+++ b/src/py/flwr_experimental/baseline/model/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/model/cnn.py b/src/py/flwr_experimental/baseline/model/cnn.py
index 429f8e40aae4..0f0d777745b2 100644
--- a/src/py/flwr_experimental/baseline/model/cnn.py
+++ b/src/py/flwr_experimental/baseline/model/cnn.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/model/cnn_test.py b/src/py/flwr_experimental/baseline/model/cnn_test.py
index 3806be494c7b..3cf23d96d961 100644
--- a/src/py/flwr_experimental/baseline/model/cnn_test.py
+++ b/src/py/flwr_experimental/baseline/model/cnn_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/model/resnet.py b/src/py/flwr_experimental/baseline/model/resnet.py
index f6095bbd9729..264822e09616 100644
--- a/src/py/flwr_experimental/baseline/model/resnet.py
+++ b/src/py/flwr_experimental/baseline/model/resnet.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/plot/__init__.py b/src/py/flwr_experimental/baseline/plot/__init__.py
index 8bc054aa44c7..bfab50defebf 100644
--- a/src/py/flwr_experimental/baseline/plot/__init__.py
+++ b/src/py/flwr_experimental/baseline/plot/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/plot/plot.py b/src/py/flwr_experimental/baseline/plot/plot.py
index d1f130a4559a..e64b2435f5c7 100644
--- a/src/py/flwr_experimental/baseline/plot/plot.py
+++ b/src/py/flwr_experimental/baseline/plot/plot.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/run.py b/src/py/flwr_experimental/baseline/run.py
index 2e6ae5b10cba..f21963c69a41 100644
--- a/src/py/flwr_experimental/baseline/run.py
+++ b/src/py/flwr_experimental/baseline/run.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/run.sh b/src/py/flwr_experimental/baseline/run.sh
index 4600d1fbc780..9cb5b76626d3 100755
--- a/src/py/flwr_experimental/baseline/run.sh
+++ b/src/py/flwr_experimental/baseline/run.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/setting.py b/src/py/flwr_experimental/baseline/setting.py
index b8987a2703ab..c47d202539ab 100644
--- a/src/py/flwr_experimental/baseline/setting.py
+++ b/src/py/flwr_experimental/baseline/setting.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_cifar/__init__.py b/src/py/flwr_experimental/baseline/tf_cifar/__init__.py
index 3651f1f42ed2..ad2e33481ba8 100644
--- a/src/py/flwr_experimental/baseline/tf_cifar/__init__.py
+++ b/src/py/flwr_experimental/baseline/tf_cifar/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_cifar/client.py b/src/py/flwr_experimental/baseline/tf_cifar/client.py
index 3b85c7e9d448..5233ee368ac8 100644
--- a/src/py/flwr_experimental/baseline/tf_cifar/client.py
+++ b/src/py/flwr_experimental/baseline/tf_cifar/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_cifar/download.py b/src/py/flwr_experimental/baseline/tf_cifar/download.py
index 7d414b205460..4beb3fb4a0f2 100644
--- a/src/py/flwr_experimental/baseline/tf_cifar/download.py
+++ b/src/py/flwr_experimental/baseline/tf_cifar/download.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_cifar/server.py b/src/py/flwr_experimental/baseline/tf_cifar/server.py
index e1f0e7f80fb6..cb7a99b9d360 100644
--- a/src/py/flwr_experimental/baseline/tf_cifar/server.py
+++ b/src/py/flwr_experimental/baseline/tf_cifar/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_cifar/settings.py b/src/py/flwr_experimental/baseline/tf_cifar/settings.py
index 4a8a86f1c56f..829856af0ace 100644
--- a/src/py/flwr_experimental/baseline/tf_cifar/settings.py
+++ b/src/py/flwr_experimental/baseline/tf_cifar/settings.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_fashion_mnist/__init__.py b/src/py/flwr_experimental/baseline/tf_fashion_mnist/__init__.py
index 4315225a7367..62c3d33eee7f 100644
--- a/src/py/flwr_experimental/baseline/tf_fashion_mnist/__init__.py
+++ b/src/py/flwr_experimental/baseline/tf_fashion_mnist/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_fashion_mnist/client.py b/src/py/flwr_experimental/baseline/tf_fashion_mnist/client.py
index b840fca79396..6177336446f1 100644
--- a/src/py/flwr_experimental/baseline/tf_fashion_mnist/client.py
+++ b/src/py/flwr_experimental/baseline/tf_fashion_mnist/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_fashion_mnist/download.py b/src/py/flwr_experimental/baseline/tf_fashion_mnist/download.py
index 385590c57699..c2f0eb580cf0 100644
--- a/src/py/flwr_experimental/baseline/tf_fashion_mnist/download.py
+++ b/src/py/flwr_experimental/baseline/tf_fashion_mnist/download.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_fashion_mnist/fn_plots.py b/src/py/flwr_experimental/baseline/tf_fashion_mnist/fn_plots.py
index 955cdb3670fb..e28d98808d8a 100644
--- a/src/py/flwr_experimental/baseline/tf_fashion_mnist/fn_plots.py
+++ b/src/py/flwr_experimental/baseline/tf_fashion_mnist/fn_plots.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_fashion_mnist/gen_plots.py b/src/py/flwr_experimental/baseline/tf_fashion_mnist/gen_plots.py
index 63e78bbf9b83..f6c0383367fd 100644
--- a/src/py/flwr_experimental/baseline/tf_fashion_mnist/gen_plots.py
+++ b/src/py/flwr_experimental/baseline/tf_fashion_mnist/gen_plots.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_fashion_mnist/server.py b/src/py/flwr_experimental/baseline/tf_fashion_mnist/server.py
index 9eea7b4d6418..fbeb0683df28 100644
--- a/src/py/flwr_experimental/baseline/tf_fashion_mnist/server.py
+++ b/src/py/flwr_experimental/baseline/tf_fashion_mnist/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py b/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py
index ec93e4ec374a..b0de9841de30 100644
--- a/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py
+++ b/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_hotkey/__init__.py b/src/py/flwr_experimental/baseline/tf_hotkey/__init__.py
index ddb094bd0d83..7ebf8a732ab5 100644
--- a/src/py/flwr_experimental/baseline/tf_hotkey/__init__.py
+++ b/src/py/flwr_experimental/baseline/tf_hotkey/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_hotkey/client.py b/src/py/flwr_experimental/baseline/tf_hotkey/client.py
index e4e1a64afd3b..f59ad5f676ba 100644
--- a/src/py/flwr_experimental/baseline/tf_hotkey/client.py
+++ b/src/py/flwr_experimental/baseline/tf_hotkey/client.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_hotkey/download.py b/src/py/flwr_experimental/baseline/tf_hotkey/download.py
index b317cd8b588c..d9f3c53bdb39 100644
--- a/src/py/flwr_experimental/baseline/tf_hotkey/download.py
+++ b/src/py/flwr_experimental/baseline/tf_hotkey/download.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_hotkey/server.py b/src/py/flwr_experimental/baseline/tf_hotkey/server.py
index a96929a0d9b4..beba81f58396 100644
--- a/src/py/flwr_experimental/baseline/tf_hotkey/server.py
+++ b/src/py/flwr_experimental/baseline/tf_hotkey/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/baseline/tf_hotkey/settings.py b/src/py/flwr_experimental/baseline/tf_hotkey/settings.py
index 9576002d2e58..18c58d1333a5 100644
--- a/src/py/flwr_experimental/baseline/tf_hotkey/settings.py
+++ b/src/py/flwr_experimental/baseline/tf_hotkey/settings.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/logserver/__init__.py b/src/py/flwr_experimental/logserver/__init__.py
index 9198841d7f0b..352112d5e933 100644
--- a/src/py/flwr_experimental/logserver/__init__.py
+++ b/src/py/flwr_experimental/logserver/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/logserver/__main__.py b/src/py/flwr_experimental/logserver/__main__.py
index 0f68a88c7ce8..a3ac56e405f0 100644
--- a/src/py/flwr_experimental/logserver/__main__.py
+++ b/src/py/flwr_experimental/logserver/__main__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/logserver/server.py b/src/py/flwr_experimental/logserver/server.py
index 993257ba5401..dc729cebbf33 100644
--- a/src/py/flwr_experimental/logserver/server.py
+++ b/src/py/flwr_experimental/logserver/server.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/logserver/server_test.py b/src/py/flwr_experimental/logserver/server_test.py
index 9440a8babbec..becead625a5d 100644
--- a/src/py/flwr_experimental/logserver/server_test.py
+++ b/src/py/flwr_experimental/logserver/server_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/__init__.py b/src/py/flwr_experimental/ops/__init__.py
index e5e609c1db95..b56c757e0207 100644
--- a/src/py/flwr_experimental/ops/__init__.py
+++ b/src/py/flwr_experimental/ops/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/cluster.py b/src/py/flwr_experimental/ops/cluster.py
index d8a063dd4f42..dfd0dd13727e 100644
--- a/src/py/flwr_experimental/ops/cluster.py
+++ b/src/py/flwr_experimental/ops/cluster.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/cluster_test.py b/src/py/flwr_experimental/ops/cluster_test.py
index ef2efd12bb99..6b00182c4d2f 100644
--- a/src/py/flwr_experimental/ops/cluster_test.py
+++ b/src/py/flwr_experimental/ops/cluster_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/compute/__init__.py b/src/py/flwr_experimental/ops/compute/__init__.py
index 208202f73f8e..f6ad468f484d 100644
--- a/src/py/flwr_experimental/ops/compute/__init__.py
+++ b/src/py/flwr_experimental/ops/compute/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/compute/adapter.py b/src/py/flwr_experimental/ops/compute/adapter.py
index 4be66ce8ed43..51c67a226a6e 100644
--- a/src/py/flwr_experimental/ops/compute/adapter.py
+++ b/src/py/flwr_experimental/ops/compute/adapter.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/compute/docker_adapter.py b/src/py/flwr_experimental/ops/compute/docker_adapter.py
index 9ce68093033a..acb7c0c5a4e0 100644
--- a/src/py/flwr_experimental/ops/compute/docker_adapter.py
+++ b/src/py/flwr_experimental/ops/compute/docker_adapter.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/compute/docker_adapter_test.py b/src/py/flwr_experimental/ops/compute/docker_adapter_test.py
index 8b59adfc5b3e..c6d5759a8246 100644
--- a/src/py/flwr_experimental/ops/compute/docker_adapter_test.py
+++ b/src/py/flwr_experimental/ops/compute/docker_adapter_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/compute/ec2_adapter.py b/src/py/flwr_experimental/ops/compute/ec2_adapter.py
index 9e8650432007..43fb66c2d944 100644
--- a/src/py/flwr_experimental/ops/compute/ec2_adapter.py
+++ b/src/py/flwr_experimental/ops/compute/ec2_adapter.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/compute/ec2_adapter_test.py b/src/py/flwr_experimental/ops/compute/ec2_adapter_test.py
index 3e2abf9fd054..14827745bf3d 100644
--- a/src/py/flwr_experimental/ops/compute/ec2_adapter_test.py
+++ b/src/py/flwr_experimental/ops/compute/ec2_adapter_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_experimental/ops/instance.py b/src/py/flwr_experimental/ops/instance.py
index eb3d368118d2..20be40552727 100644
--- a/src/py/flwr_experimental/ops/instance.py
+++ b/src/py/flwr_experimental/ops/instance.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_tool/__init__.py b/src/py/flwr_tool/__init__.py
index 50e3d8645b6d..f85580898a5e 100644
--- a/src/py/flwr_tool/__init__.py
+++ b/src/py/flwr_tool/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_tool/init_py_check.py b/src/py/flwr_tool/init_py_check.py
index 60acfabb22b4..8cdc2e0ab5be 100755
--- a/src/py/flwr_tool/init_py_check.py
+++ b/src/py/flwr_tool/init_py_check.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
 """Check provided directory and sub-directories for missing __init__.py files.
 
 Example:
diff --git a/src/py/flwr_tool/protoc.py b/src/py/flwr_tool/protoc.py
index b571279f0707..5d3ce942c1e0 100644
--- a/src/py/flwr_tool/protoc.py
+++ b/src/py/flwr_tool/protoc.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/py/flwr_tool/protoc_test.py b/src/py/flwr_tool/protoc_test.py
index 07e7dd833e9d..57ca3ff423c2 100644
--- a/src/py/flwr_tool/protoc_test.py
+++ b/src/py/flwr_tool/protoc_test.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap GmbH. All Rights Reserved.
+# 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.
diff --git a/src/swift/flwr/Sources/Flower/FlowerProto/transport.pb.swift b/src/swift/flwr/Sources/Flower/FlowerProto/transport.pb.swift
index 398d2834e370..d1d17e5c875c 100644
--- a/src/swift/flwr/Sources/Flower/FlowerProto/transport.pb.swift
+++ b/src/swift/flwr/Sources/Flower/FlowerProto/transport.pb.swift
@@ -7,7 +7,7 @@
 // For information on using the generated types, please see the documentation:
 //   https://github.com/apple/swift-protobuf/
 
-// Copyright 2020 Adap GmbH. All Rights Reserved.
+// 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.

From 0be08301f6312be3273dc2169ccddec45502e9dc Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Mon, 9 Oct 2023 10:18:52 +0200
Subject: [PATCH 43/60] Update quickstart-huggingface example requirements
 (#2465)

---------

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
Co-authored-by: Taner Topal <taner@flower.dev>
---
 examples/quickstart-huggingface/pyproject.toml   | 14 +++++++-------
 examples/quickstart-huggingface/requirements.txt | 12 ++++++------
 2 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/examples/quickstart-huggingface/pyproject.toml b/examples/quickstart-huggingface/pyproject.toml
index 760dbf114cfa..eb9687c5152c 100644
--- a/examples/quickstart-huggingface/pyproject.toml
+++ b/examples/quickstart-huggingface/pyproject.toml
@@ -12,10 +12,10 @@ authors = [
 ]
 
 [tool.poetry.dependencies]
-python = "^3.8"
-flwr = "^1.0.0"
-torch = "^1.13.1"
-transformers = "4.30.0"
-datasets = "1.12.1"
-evaluate = "0.4.0"
-scikit-learn = "1.0"
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+torch = ">=1.13.1,<2.0"
+transformers = ">=4.30.0,<5.0"
+evaluate = ">=0.4.0,<1.0"
+datasets = ">=2.0.0, <3.0"
+scikit-learn = ">=1.3.1, <2.0"
diff --git a/examples/quickstart-huggingface/requirements.txt b/examples/quickstart-huggingface/requirements.txt
index 865cac252fd9..aeb2d13fc4a4 100644
--- a/examples/quickstart-huggingface/requirements.txt
+++ b/examples/quickstart-huggingface/requirements.txt
@@ -1,6 +1,6 @@
-datasets~=2.13.1
-evaluate~=0.4.0
-flwr~=1.4.0
-numpy~=1.21.1
-torch~=2.0.1
-transformers~=4.30.2
+flwr>=1.0, <2.0
+torch>=1.13.1, <2.0
+transformers>=4.30.0, <5.0
+evaluate>=0.4.0, <1.0
+datasets>=2.0.0, <3.0
+scikit-learn>=1.3.1, <2.0

From 908d539bb4f76dcae5257d2a7683f266a4bf8357 Mon Sep 17 00:00:00 2001
From: Marius Schlegel <85943265+mariusschlegel@users.noreply.github.com>
Date: Mon, 9 Oct 2023 10:28:42 +0200
Subject: [PATCH 44/60] Fix code example using TensorFlow/Keras + MLCube
 (#2425)

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 .gitignore                                    |   2 +-
 doc/source/ref-changelog.md                   |   4 +-
 examples/quickstart-mlcube/README.md          |  28 +-
 examples/quickstart-mlcube/dev/Dockerfile     |  18 ++
 examples/quickstart-mlcube/dev/all.sh         |  19 ++
 examples/quickstart-mlcube/dev/client.sh      |  11 +
 examples/quickstart-mlcube/dev/mnist.py       | 282 ++++++++++++++++++
 .../quickstart-mlcube/dev/requirements.txt    |   4 +
 examples/quickstart-mlcube/dev/server.sh      |   6 +
 examples/quickstart-mlcube/dev/setup.sh       |  18 ++
 examples/quickstart-mlcube/pyproject.toml     |   4 +-
 examples/quickstart-mlcube/requirements.txt   |   4 +-
 12 files changed, 390 insertions(+), 10 deletions(-)
 create mode 100644 examples/quickstart-mlcube/dev/Dockerfile
 create mode 100755 examples/quickstart-mlcube/dev/all.sh
 create mode 100755 examples/quickstart-mlcube/dev/client.sh
 create mode 100644 examples/quickstart-mlcube/dev/mnist.py
 create mode 100644 examples/quickstart-mlcube/dev/requirements.txt
 create mode 100755 examples/quickstart-mlcube/dev/server.sh
 create mode 100755 examples/quickstart-mlcube/dev/setup.sh

diff --git a/.gitignore b/.gitignore
index d7de7de3c3eb..7a152abdc715 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,7 +11,7 @@ node_modules
 
 # Flower Examples
 examples/**/dataset/**
-examples/**/dev/*.sh
+#examples/**/dev/*.sh
 
 # Flower Baselines
 baselines/datasets/leaf
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index eefd215503f8..47685f201f2b 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -20,9 +20,9 @@
 
   - FedProx ([#2210](https://github.com/adap/flower/pull/2210), [#2286](https://github.com/adap/flower/pull/2286))
 
-- **Update Flower Examples** ([#2384](https://github.com/adap/flower/pull/2384))
+- **Update Flower Examples** ([#2384](https://github.com/adap/flower/pull/2384)), ([#2425](https://github.com/adap/flower/pull/2425))
 
-- **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))
+- **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))
 
 - **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331), [#2448](https://github.com/adap/flower/pull/2448))
 
diff --git a/examples/quickstart-mlcube/README.md b/examples/quickstart-mlcube/README.md
index b7994a0d0631..8e6fc29b3ad8 100644
--- a/examples/quickstart-mlcube/README.md
+++ b/examples/quickstart-mlcube/README.md
@@ -10,7 +10,11 @@ Start by cloning the example project. We prepared a single-line command that you
 git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/quickstart-mlcube . && rm -rf flower && cd quickstart-mlcube
 ```
 
-Project dependencies (such as `tensorflow` and `flwr`) are defined in `pyproject.toml`. 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)), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.
+### Installing Dependencies
+
+Project dependencies (such as `tensorflow` 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
@@ -23,11 +27,29 @@ Poetry will install all your dependencies in a newly created virtual environment
 poetry run python3 -c "import flwr"
 ```
 
-For the MLCube setup you will need to install Docker on your system. Please refer to the [Docker install guide](https://docs.docker.com/get-docker/) on how to do that.
+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
+```
+
+To verify that everything works correctly, you can run the following command:
+
+```shell
+python3 -c "import flwr"
+```
 
 If you don't see any errors, you're good to go!
 
-## MLCube setup
+#### Docker
+
+For the MLCube setup you will need to install Docker on your system. Please refer to the [Docker install guide](https://docs.docker.com/get-docker/) on how to do that.
+
+#### MLCube
 
 For the MLCube setup, we have prepared a script that you can execute in your shell using:
 
diff --git a/examples/quickstart-mlcube/dev/Dockerfile b/examples/quickstart-mlcube/dev/Dockerfile
new file mode 100644
index 000000000000..98df9655d70d
--- /dev/null
+++ b/examples/quickstart-mlcube/dev/Dockerfile
@@ -0,0 +1,18 @@
+FROM ubuntu:22.04
+LABEL author="The Flower Authors"
+
+RUN apt-get update && \
+    # install Python
+    apt-get install -y python3 python3-distutils curl && \
+    update-alternatives --install /usr/bin/python python /usr/bin/python3 1 && \
+    curl -sSL -O https://bootstrap.pypa.io/get-pip.py && \
+    python3 get-pip.py && \
+    rm get-pip.py && \
+    # clean apt cache
+    rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt /requirements.txt
+RUN pip3 install --no-cache-dir -r /requirements.txt
+
+COPY mnist.py /workspace/mnist.py
+ENTRYPOINT ["python3", "/workspace/mnist.py"]
diff --git a/examples/quickstart-mlcube/dev/all.sh b/examples/quickstart-mlcube/dev/all.sh
new file mode 100755
index 000000000000..993e3a0f5d56
--- /dev/null
+++ b/examples/quickstart-mlcube/dev/all.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
+
+# Cleanup & prepare mlcube
+./dev/setup.sh
+
+# Start server
+./dev/server.sh &
+sleep 2 # Sleep for 2s to give the server enough time to start
+
+for i in `seq 0 1`; do
+    ./dev/client.sh $i &
+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/examples/quickstart-mlcube/dev/client.sh b/examples/quickstart-mlcube/dev/client.sh
new file mode 100755
index 000000000000..54e506be7446
--- /dev/null
+++ b/examples/quickstart-mlcube/dev/client.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
+
+if [ $# -eq 0 ]
+  then
+    echo "Please provide the name of the client workspace e.g. 'python client.py one'"
+    exit 1
+fi
+
+poetry run python client.py $1
diff --git a/examples/quickstart-mlcube/dev/mnist.py b/examples/quickstart-mlcube/dev/mnist.py
new file mode 100644
index 000000000000..e52e2cba85c7
--- /dev/null
+++ b/examples/quickstart-mlcube/dev/mnist.py
@@ -0,0 +1,282 @@
+"""
+https://www.tensorflow.org/tutorials/quickstart/beginner
+"""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import argparse
+import json
+import logging
+import logging.config
+import os
+import sys
+from enum import Enum
+from typing import List
+
+import numpy as np
+import tensorflow as tf
+import yaml
+from tensorflow.keras.utils import get_file
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"  # Remove warning messages
+
+logger = logging.getLogger(__name__)
+
+
+class Task(str, Enum):
+    DownloadData = "download"
+    Train = "train"
+    Evaluate = "evaluate"
+
+
+def create_directory(path: str) -> None:
+    if not os.path.exists(path):
+        os.makedirs(path, exist_ok=True)
+
+
+def download(task_args: List[str]) -> None:
+    """Task: download.
+    Input parameters:
+        --data_dir
+    """
+    logger.info(f"Starting '{Task.DownloadData}' task")
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--data_dir",
+        "--data-dir",
+        type=str,
+        default=None,
+        help="Path to a dataset file.",
+    )
+    args = parser.parse_args(args=task_args)
+
+    if args.data_dir is None:
+        raise ValueError(
+            "Data directory is not specified (did you use --data-dir=PATH?)"
+        )
+    os.makedirs(args.data_dir, exist_ok=True)
+
+    if not args.data_dir.startswith("/"):
+        logger.warning("Data directory seems to be a relative path.")
+
+    data_file = os.path.join(args.data_dir, "mnist.npz")
+    if os.path.exists(data_file):
+        logger.info("MNIST data has already been download (file exists: %s)", data_file)
+        return
+
+    data_file = get_file(
+        fname=data_file,
+        origin="https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz",
+        file_hash="731c5ac602752760c8e48fbffcf8c3b850d9dc2a2aedcf2cc48468fc17b673d1",
+    )
+
+    if not os.path.isfile(data_file):
+        raise ValueError(
+            f"MNIST dataset has not been downloaded - dataset file does not exist: {data_file}"
+        )
+    else:
+        logger.info("MNIST dataset has been downloaded.")
+    logger.info("The '%s' task has been completed.", Task.DownloadData)
+
+
+def train(task_args: List[str]) -> None:
+    """Task: train.
+    Input parameters:
+        --data_dir, --log_dir, --model_dir, --parameters_file
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--data_dir", "--data-dir", type=str, default=None, help="Dataset path."
+    )
+    parser.add_argument(
+        "--model_in",
+        "--model-in",
+        type=str,
+        default=None,
+        help="Model output directory.",
+    )
+    parser.add_argument(
+        "--model_dir",
+        "--model-dir",
+        type=str,
+        default=None,
+        help="Model output directory.",
+    )
+    parser.add_argument(
+        "--parameters_file",
+        "--parameters-file",
+        type=str,
+        default=None,
+        help="Parameters default values.",
+    )
+    parser.add_argument(
+        "--metrics",
+        "--metrics",
+        type=str,
+        default=None,
+        help="Parameters default values.",
+    )
+
+    args = parser.parse_args(args=task_args)
+
+    with open(args.parameters_file, "r") as stream:
+        parameters = yaml.load(stream, Loader=yaml.FullLoader)
+    logger.info("Parameters have been read (%s).", args.parameters_file)
+
+    dataset_file = os.path.join(args.data_dir, "mnist.npz")
+    with np.load(dataset_file, allow_pickle=True) as f:
+        x_train, y_train = f["x_train"], f["y_train"]
+    x_train = x_train / 255.0
+    logger.info("Dataset has been loaded (%s).", dataset_file)
+
+    if args.model_in != "" and len(os.listdir(args.model_in)) != 0:
+        # Load from checkpoint;
+        model = tf.keras.models.load_model(os.path.join(args.model_in, "mnist_model"))
+    else:
+        # if no model given on CLI, create a new one
+        model = tf.keras.models.Sequential(
+            [
+                tf.keras.layers.Flatten(input_shape=(28, 28)),
+                tf.keras.layers.Dense(128, activation="relu"),
+                tf.keras.layers.Dropout(0.2),
+                tf.keras.layers.Dense(10, activation="softmax"),
+            ]
+        )
+
+    logger.info("Model has been built.")
+
+    model.compile(
+        optimizer=parameters.get("optimizer", "adam"),
+        loss="sparse_categorical_crossentropy",
+        metrics=["accuracy"],
+    )
+    logger.info("Model has been compiled.")
+
+    # Train and evaluate
+    history = model.fit(
+        x_train,
+        y_train,
+        batch_size=parameters.get("batch_size", 32),
+        epochs=parameters.get("epochs", 5),
+    )
+    logger.info("Model has been trained.")
+
+    with open(args.metrics, "w") as f:
+        data_json = {
+            "loss": str(history.history["loss"][-1]),
+            "accuracy": str(history.history["accuracy"][-1]),
+        }
+        json.dump(data_json, f)
+
+    os.makedirs(args.model_dir, exist_ok=True)
+    model.save(os.path.join(args.model_dir, "mnist_model"))
+    logger.info("Model has been saved.")
+
+
+def evaluate(task_args: List[str]) -> None:
+    """Task: train.
+    Input parameters:
+        --data_dir, --log_dir, --model_dir, --parameters_file
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--data_dir", "--data-dir", type=str, default=None, help="Dataset path."
+    )
+    parser.add_argument(
+        "--model_in",
+        "--model-in",
+        type=str,
+        default=None,
+        help="Model output directory.",
+    )
+    parser.add_argument(
+        "--parameters_file",
+        "--parameters-file",
+        type=str,
+        default=None,
+        help="Parameters default values.",
+    )
+    parser.add_argument(
+        "--metrics",
+        "--metrics",
+        type=str,
+        default=None,
+        help="Parameters default values.",
+    )
+    args = parser.parse_args(args=task_args)
+
+    with open(args.parameters_file, "r") as stream:
+        parameters = yaml.load(stream, Loader=yaml.FullLoader)
+        logger.info("Parameters have been read (%s).", parameters)
+
+    dataset_file = os.path.join(args.data_dir, "mnist.npz")
+    with np.load(dataset_file, allow_pickle=True) as f:
+        x_test, y_test = f["x_test"], f["y_test"]
+    x_test = x_test / 255.0
+    logger.info("Dataset has been loaded (%s).", dataset_file)
+
+    model = tf.keras.models.load_model(os.path.join(args.model_in, "mnist_model"))
+
+    eval_result = model.evaluate(x_test, y_test)
+
+    with open(args.metrics, "w") as f:
+        data_json = {"loss": str(eval_result[0]), "accuracy": str(eval_result[1])}
+        json.dump(data_json, f)
+
+    logger.info("Model has been evaluated.")
+
+
+def main():
+    """mnist.py task task_specific_parameters..."""
+    # noinspection PyBroadException
+    try:
+        parser = argparse.ArgumentParser()
+        parser.add_argument("mlcube_task", type=str, help="Task for this MLCube.")
+        parser.add_argument(
+            "--log_dir", "--log-dir", type=str, required=True, help="Logging directory."
+        )
+        mlcube_args, task_args = parser.parse_known_args()
+
+        os.makedirs(mlcube_args.log_dir, exist_ok=True)
+        logger_config = {
+            "version": 1,
+            "disable_existing_loggers": True,
+            "formatters": {
+                "standard": {
+                    "format": "%(asctime)s - %(name)s - %(threadName)s - %(levelname)s - %(message)s"
+                },
+            },
+            "handlers": {
+                "file_handler": {
+                    "class": "logging.FileHandler",
+                    "level": "INFO",
+                    "formatter": "standard",
+                    "filename": os.path.join(
+                        mlcube_args.log_dir,
+                        f"mlcube_mnist_{mlcube_args.mlcube_task}.log",
+                    ),
+                }
+            },
+            "loggers": {
+                "": {"level": "INFO", "handlers": ["file_handler"]},
+                "__main__": {"level": "NOTSET", "propagate": "yes"},
+                "tensorflow": {"level": "NOTSET", "propagate": "yes"},
+            },
+        }
+        logging.config.dictConfig(logger_config)
+
+        if mlcube_args.mlcube_task == Task.DownloadData:
+            download(task_args)
+        elif mlcube_args.mlcube_task == Task.Train:
+            train(task_args)
+        elif mlcube_args.mlcube_task == Task.Evaluate:
+            evaluate(task_args)
+        else:
+            raise ValueError(f"Unknown task: {task_args}")
+    except Exception as err:
+        logger.exception(err)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/quickstart-mlcube/dev/requirements.txt b/examples/quickstart-mlcube/dev/requirements.txt
new file mode 100644
index 000000000000..c39a5fa73f81
--- /dev/null
+++ b/examples/quickstart-mlcube/dev/requirements.txt
@@ -0,0 +1,4 @@
+PyYAML==5.3
+tensorflow==2.14.0
+tensorflow-estimator==2.14.0
+requests[security]
diff --git a/examples/quickstart-mlcube/dev/server.sh b/examples/quickstart-mlcube/dev/server.sh
new file mode 100755
index 000000000000..8a0f5857f198
--- /dev/null
+++ b/examples/quickstart-mlcube/dev/server.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
+
+# Start server
+poetry run python server.py
diff --git a/examples/quickstart-mlcube/dev/setup.sh b/examples/quickstart-mlcube/dev/setup.sh
new file mode 100755
index 000000000000..087ca94020c9
--- /dev/null
+++ b/examples/quickstart-mlcube/dev/setup.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
+
+# Cleanup
+echo "Cleanup mlcube and clients directory"
+rm -rf mlcube
+
+# Clone mlcube
+echo "Clone mlcube from GitHub"
+git clone https://github.com/mlcommons/mlcube_examples.git
+cp -r mlcube_examples/mnist_fl/tensorflow mlcube
+rm -rf mlcube_examples
+cp dev/{Dockerfile,requirements.txt,mnist.py} mlcube/build
+
+# Download dataset
+cd mlcube
+mlcube configure -Pdocker.build_strategy=auto
diff --git a/examples/quickstart-mlcube/pyproject.toml b/examples/quickstart-mlcube/pyproject.toml
index 156a9635528b..0d42fc3b2898 100644
--- a/examples/quickstart-mlcube/pyproject.toml
+++ b/examples/quickstart-mlcube/pyproject.toml
@@ -13,6 +13,6 @@ python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"  # For development: { path = "../../", develop = true }
 tensorflow-cpu = {version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "platform_machine == \"x86_64\""}
 tensorflow-macos = {version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "sys_platform == \"darwin\" and platform_machine == \"arm64\""}
-mlcube = "0.0.8"
-mlcube-docker = "0.0.8"
+mlcube = "0.0.9"
+mlcube-docker = "0.0.9"
 tensorflow-estimator = ">=2.9.1,<2.11.1 || >2.11.1"
diff --git a/examples/quickstart-mlcube/requirements.txt b/examples/quickstart-mlcube/requirements.txt
index bfcf0ff1e115..af8514bc7392 100644
--- a/examples/quickstart-mlcube/requirements.txt
+++ b/examples/quickstart-mlcube/requirements.txt
@@ -1,6 +1,6 @@
 flwr>=1.0, <2.0
 tensorflow-macos>=2.9.1, != 2.11.1 ; sys_platform == "darwin" and platform_machine == "arm64"
 tensorflow-cpu>=2.9.1, != 2.11.1 ; platform_machine == "x86_64"
-mlcube==0.0.8
-mlcube-docker==0.0.8
+mlcube==0.0.9
+mlcube-docker==0.0.9
 tensorflow-estimator>=2.9.1, !=2.11.1

From 00f0d7709a7eced5fa3e28c43bae99841dfc47aa Mon Sep 17 00:00:00 2001
From: Yan Gao <y.gaogy@gmail.com>
Date: Mon, 9 Oct 2023 19:18:04 +0100
Subject: [PATCH 45/60] Clarify pytorch-lightning README (#2494)

Co-authored-by: yan-gao-GY <yan@flower.dev>
---
 doc/source/tutorial-quickstart-pytorch-lightning.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/tutorial-quickstart-pytorch-lightning.rst b/doc/source/tutorial-quickstart-pytorch-lightning.rst
index b8d5e50c4714..acfbecf41260 100644
--- a/doc/source/tutorial-quickstart-pytorch-lightning.rst
+++ b/doc/source/tutorial-quickstart-pytorch-lightning.rst
@@ -7,6 +7,6 @@ Quickstart PyTorch Lightning
 .. meta::
    :description: Check out this Federated Learning quickstart tutorial for using Flower with PyTorch Lightning to train an Auto Encoder model on MNIST.
 
-Let's build a federated learning system using PyTorch Lightning and Flower!
+Let's build a horizontal federated learning system using PyTorch Lightning and Flower!
 
 Please refer to the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-lightning>`_ to learn more.

From 8fbfaf86169e827047707eaf5ae1d81a7b77ce8b Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Mon, 9 Oct 2023 21:08:43 +0200
Subject: [PATCH 46/60] Enable more datasets in FDS (#2392)

---
 datasets/flwr_datasets/federated_dataset.py   |  4 +--
 .../flwr_datasets/federated_dataset_test.py   |  2 +-
 datasets/flwr_datasets/utils.py               | 26 ++++++++++---------
 3 files changed, 17 insertions(+), 15 deletions(-)

diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py
index bfd7ceff70b1..4dcf3804099c 100644
--- a/datasets/flwr_datasets/federated_dataset.py
+++ b/datasets/flwr_datasets/federated_dataset.py
@@ -20,7 +20,7 @@
 import datasets
 from datasets import Dataset, DatasetDict
 from flwr_datasets.partitioner import Partitioner
-from flwr_datasets.utils import _check_if_dataset_supported, _instantiate_partitioners
+from flwr_datasets.utils import _check_if_dataset_tested, _instantiate_partitioners
 
 
 class FederatedDataset:
@@ -54,7 +54,7 @@ class FederatedDataset:
     """
 
     def __init__(self, *, dataset: str, partitioners: Dict[str, int]) -> None:
-        _check_if_dataset_supported(dataset)
+        _check_if_dataset_tested(dataset)
         self._dataset_name: str = dataset
         self._partitioners: Dict[str, Partitioner] = _instantiate_partitioners(
             partitioners
diff --git a/datasets/flwr_datasets/federated_dataset_test.py b/datasets/flwr_datasets/federated_dataset_test.py
index 467f9d856ed4..57483dfba79f 100644
--- a/datasets/flwr_datasets/federated_dataset_test.py
+++ b/datasets/flwr_datasets/federated_dataset_test.py
@@ -112,7 +112,7 @@ def test_no_split_in_the_dataset(self) -> None:  # pylint: disable=R0201
 
     def test_unsupported_dataset(self) -> None:  # pylint: disable=R0201
         """Test creating FederatedDataset for unsupported dataset."""
-        with pytest.raises(ValueError):
+        with pytest.warns(UserWarning):
             FederatedDataset(dataset="food101", partitioners={"train": 100})
 
 
diff --git a/datasets/flwr_datasets/utils.py b/datasets/flwr_datasets/utils.py
index 734594b77033..60290c461203 100644
--- a/datasets/flwr_datasets/utils.py
+++ b/datasets/flwr_datasets/utils.py
@@ -15,10 +15,19 @@
 """Utils for FederatedDataset."""
 
 
+import warnings
 from typing import Dict
 
 from flwr_datasets.partitioner import IidPartitioner, Partitioner
 
+tested_datasets = [
+    "mnist",
+    "cifar10",
+    "fashion_mnist",
+    "sasha/dog-food",
+    "zh-plus/tiny-imagenet",
+]
+
 
 def _instantiate_partitioners(partitioners: Dict[str, int]) -> Dict[str, Partitioner]:
     """Transform the partitioners from the initial format to instantiated objects.
@@ -41,17 +50,10 @@ def _instantiate_partitioners(partitioners: Dict[str, int]) -> Dict[str, Partiti
     return instantiated_partitioners
 
 
-def _check_if_dataset_supported(dataset: str) -> None:
+def _check_if_dataset_tested(dataset: str) -> None:
     """Check if the dataset is in the narrowed down list of the tested datasets."""
-    supported_datasets = [
-        "mnist",
-        "cifar10",
-        "fashion_mnist",
-        "sasha/dog-food",
-        "zh-plus/tiny-imagenet",
-    ]
-    if dataset not in supported_datasets:
-        raise ValueError(
-            f"The currently tested and supported dataset are {supported_datasets}. "
-            f"Given: {dataset}"
+    if dataset not in tested_datasets:
+        warnings.warn(
+            f"The currently tested dataset are {tested_datasets}. Given: {dataset}.",
+            stacklevel=1,
         )

From 9c7ad6fcb277ec9a066a03bef86a21c3444c63e5 Mon Sep 17 00:00:00 2001
From: Shaz-hash <73599731+Shaz-hash@users.noreply.github.com>
Date: Tue, 10 Oct 2023 14:48:24 +0500
Subject: [PATCH 47/60] Update Gradle files (#2430)

---
 examples/android/client/app/build.gradle | 4 +++-
 examples/android/client/build.gradle     | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/android/client/app/build.gradle b/examples/android/client/app/build.gradle
index abb2f5109d03..7b9e844105c6 100644
--- a/examples/android/client/app/build.gradle
+++ b/examples/android/client/app/build.gradle
@@ -44,7 +44,7 @@ android {
 def grpc_version = '1.43.0'
 
 protobuf {
-    protoc { artifact = 'com.google.protobuf:protoc:3.11.0' }
+    protoc { artifact = 'com.google.protobuf:protoc:3.17.3' }
     plugins {
         grpc { artifact = "io.grpc:protoc-gen-grpc-java:$grpc_version"
         }
@@ -66,6 +66,7 @@ dependencies {
     implementation 'androidx.appcompat:appcompat:1.6.0'
     implementation fileTree(dir: 'libs', include: ['*.jar'])
     implementation 'androidx.constraintlayout:constraintlayout:2.1.4'
+    implementation 'androidx.work:work-runtime:2.8.1'
     testImplementation 'junit:junit:4.13.2'
     androidTestImplementation 'androidx.test:runner:1.5.2'
     androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
@@ -85,6 +86,7 @@ dependencies {
     implementation "androidx.lifecycle:lifecycle-common-java8:$lifecycle_version"
 
     implementation 'com.google.android.material:material:1.7.0'
+    implementation 'com.google.protobuf:protobuf-javalite:3.17.3'
 
 }
 
diff --git a/examples/android/client/build.gradle b/examples/android/client/build.gradle
index 70c06f5b18b7..4fac366bd918 100644
--- a/examples/android/client/build.gradle
+++ b/examples/android/client/build.gradle
@@ -6,7 +6,7 @@ buildscript {
         jcenter()
     }
     dependencies {
-        classpath 'com.android.tools.build:gradle:7.4.0'
+        classpath 'com.android.tools.build:gradle:7.4.1'
         classpath "com.google.protobuf:protobuf-gradle-plugin:0.9.2"
 
         // NOTE: Do not place your application dependencies here; they belong

From acf79022640c88b2abe93710a064b7b85177ee21 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 10 Oct 2023 13:11:27 +0100
Subject: [PATCH 48/60] Add `to_client` method to client classes (#2493)

Co-authored-by: Daniel J. Beutel <daniel@flower.dev>
---
 doc/source/ref-changelog.md                |   2 +-
 src/py/flwr/client/app.py                  |  15 ++-
 src/py/flwr/client/client.py               |   6 +
 src/py/flwr/client/numpy_client.py         | 150 ++++++++++++++++++++-
 src/py/flwr/client/numpy_client_wrapper.py | 145 +-------------------
 5 files changed, 168 insertions(+), 150 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 47685f201f2b..8ee97a955fea 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -26,7 +26,7 @@
 
 - **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331), [#2448](https://github.com/adap/flower/pull/2448))
 
-- **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))
+- **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), [#2493](https://github.com/adap/flower/pull/2493))
 
   Flower received many improvements under the hood, too many to list here.
 
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index b4866bf60ead..880e2e80dbef 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -20,6 +20,7 @@
 from logging import INFO
 from typing import Callable, Optional, Union
 
+from flwr.client.client import Client
 from flwr.client.typing import ClientFn, ClientLike
 from flwr.common import GRPC_MAX_MESSAGE_LENGTH, EventType, event
 from flwr.common.address import parse_address
@@ -36,7 +37,6 @@
 from .grpc_rere_client.connection import grpc_request_response
 from .message_handler.message_handler import handle
 from .numpy_client import NumPyClient
-from .numpy_client_wrapper import _wrap_numpy_client
 
 
 def _check_actionable_client(
@@ -271,10 +271,19 @@ def start_numpy_client(
     # Start
     _check_actionable_client(client, client_fn)
 
-    wrp_client = _wrap_numpy_client(client=client) if client else None
+    wrp_client = client.to_client() if client else None
+    wrp_clientfn = None
+    if client_fn:
+
+        def convert(cid: str) -> Client:
+            """Convert `NumPyClient` to `Client` upon instantiation."""
+            return client_fn(cid).to_client()
+
+        wrp_clientfn = convert
+
     start_client(
         server_address=server_address,
-        client_fn=client_fn,
+        client_fn=wrp_clientfn,
         client=wrp_client,
         grpc_max_message_length=grpc_max_message_length,
         root_certificates=root_certificates,
diff --git a/src/py/flwr/client/client.py b/src/py/flwr/client/client.py
index a0ee3a3e94b8..03769c6b8bcf 100644
--- a/src/py/flwr/client/client.py
+++ b/src/py/flwr/client/client.py
@@ -14,6 +14,8 @@
 # ==============================================================================
 """Flower client (abstract base class)."""
 
+# Needed to `Client` class can return a type of `Client` (not needed in py3.11+)
+from __future__ import annotations
 
 from abc import ABC
 
@@ -136,6 +138,10 @@ def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
             metrics={},
         )
 
+    def to_client(self) -> Client:
+        """Return client (itself)."""
+        return self
+
 
 def has_get_properties(client: Client) -> bool:
     """Check if Client implements get_properties."""
diff --git a/src/py/flwr/client/numpy_client.py b/src/py/flwr/client/numpy_client.py
index b535015a9c1f..883be2f3d554 100644
--- a/src/py/flwr/client/numpy_client.py
+++ b/src/py/flwr/client/numpy_client.py
@@ -16,9 +16,54 @@
 
 
 from abc import ABC
-from typing import Dict, Tuple
+from typing import Callable, Dict, Tuple
 
-from flwr.common import Config, NDArrays, Scalar
+from flwr.client.client import Client
+from flwr.common import (
+    Config,
+    NDArrays,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.common.typing import (
+    Code,
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    GetParametersIns,
+    GetParametersRes,
+    GetPropertiesIns,
+    GetPropertiesRes,
+    Status,
+)
+
+EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_FIT = """
+NumPyClient.fit did not return a tuple with 3 elements.
+The returned values should have the following type signature:
+
+    Tuple[NDArrays, int, Dict[str, Scalar]]
+
+Example
+-------
+
+    model.get_weights(), 10, {"accuracy": 0.95}
+
+"""
+
+EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_EVALUATE = """
+NumPyClient.evaluate did not return a tuple with 3 elements.
+The returned values should have the following type signature:
+
+    Tuple[float, int, Dict[str, Scalar]]
+
+Example
+-------
+
+    0.5, 10, {"accuracy": 0.95}
+
+"""
 
 
 class NumPyClient(ABC):
@@ -126,6 +171,10 @@ def evaluate(
         _ = (self, parameters, config)
         return 0.0, 0, {}
 
+    def to_client(self) -> Client:
+        """Convert to object to Client type and return it."""
+        return _wrap_numpy_client(client=self)
+
 
 def has_get_properties(client: NumPyClient) -> bool:
     """Check if NumPyClient implements get_properties."""
@@ -145,3 +194,100 @@ def has_fit(client: NumPyClient) -> bool:
 def has_evaluate(client: NumPyClient) -> bool:
     """Check if NumPyClient implements evaluate."""
     return type(client).evaluate != NumPyClient.evaluate
+
+
+def _constructor(self: Client, numpy_client: NumPyClient) -> None:
+    self.numpy_client = numpy_client  # type: ignore
+
+
+def _get_properties(self: Client, ins: GetPropertiesIns) -> GetPropertiesRes:
+    """Return the current client properties."""
+    properties = self.numpy_client.get_properties(config=ins.config)  # type: ignore
+    return GetPropertiesRes(
+        status=Status(code=Code.OK, message="Success"),
+        properties=properties,
+    )
+
+
+def _get_parameters(self: Client, ins: GetParametersIns) -> GetParametersRes:
+    """Return the current local model parameters."""
+    parameters = self.numpy_client.get_parameters(config=ins.config)  # type: ignore
+    parameters_proto = ndarrays_to_parameters(parameters)
+    return GetParametersRes(
+        status=Status(code=Code.OK, message="Success"), parameters=parameters_proto
+    )
+
+
+def _fit(self: Client, ins: FitIns) -> FitRes:
+    """Refine the provided parameters using the locally held dataset."""
+    # Deconstruct FitIns
+    parameters: NDArrays = parameters_to_ndarrays(ins.parameters)
+
+    # Train
+    results = self.numpy_client.fit(parameters, ins.config)  # type: ignore
+    if not (
+        len(results) == 3
+        and isinstance(results[0], list)
+        and isinstance(results[1], int)
+        and isinstance(results[2], dict)
+    ):
+        raise Exception(EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_FIT)
+
+    # Return FitRes
+    parameters_prime, num_examples, metrics = results
+    parameters_prime_proto = ndarrays_to_parameters(parameters_prime)
+    return FitRes(
+        status=Status(code=Code.OK, message="Success"),
+        parameters=parameters_prime_proto,
+        num_examples=num_examples,
+        metrics=metrics,
+    )
+
+
+def _evaluate(self: Client, ins: EvaluateIns) -> EvaluateRes:
+    """Evaluate the provided parameters using the locally held dataset."""
+    parameters: NDArrays = parameters_to_ndarrays(ins.parameters)
+
+    results = self.numpy_client.evaluate(parameters, ins.config)  # type: ignore
+    if not (
+        len(results) == 3
+        and isinstance(results[0], float)
+        and isinstance(results[1], int)
+        and isinstance(results[2], dict)
+    ):
+        raise Exception(EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_EVALUATE)
+
+    # Return EvaluateRes
+    loss, num_examples, metrics = results
+    return EvaluateRes(
+        status=Status(code=Code.OK, message="Success"),
+        loss=loss,
+        num_examples=num_examples,
+        metrics=metrics,
+    )
+
+
+def _wrap_numpy_client(client: NumPyClient) -> Client:
+    member_dict: Dict[str, Callable] = {  # type: ignore
+        "__init__": _constructor,
+    }
+
+    # Add wrapper type methods (if overridden)
+
+    if has_get_properties(client=client):
+        member_dict["get_properties"] = _get_properties
+
+    if has_get_parameters(client=client):
+        member_dict["get_parameters"] = _get_parameters
+
+    if has_fit(client=client):
+        member_dict["fit"] = _fit
+
+    if has_evaluate(client=client):
+        member_dict["evaluate"] = _evaluate
+
+    # Create wrapper class
+    wrapper_class = type("NumPyClientWrapper", (Client,), member_dict)
+
+    # Create and return an instance of the newly created class
+    return wrapper_class(numpy_client=client)  # type: ignore
diff --git a/src/py/flwr/client/numpy_client_wrapper.py b/src/py/flwr/client/numpy_client_wrapper.py
index 9e7219f8e842..cfdfb6cf607c 100644
--- a/src/py/flwr/client/numpy_client_wrapper.py
+++ b/src/py/flwr/client/numpy_client_wrapper.py
@@ -14,56 +14,10 @@
 # ==============================================================================
 """Wrapper for NumPyClient objects."""
 
-from typing import Callable, Dict
-
 from flwr.client.typing import ClientLike
-from flwr.common import ndarrays_to_parameters, parameters_to_ndarrays
-from flwr.common.typing import (
-    Code,
-    EvaluateIns,
-    EvaluateRes,
-    FitIns,
-    FitRes,
-    GetParametersIns,
-    GetParametersRes,
-    GetPropertiesIns,
-    GetPropertiesRes,
-    NDArrays,
-    Status,
-)
 
 from .client import Client
-from .numpy_client import NumPyClient
-from .numpy_client import has_evaluate as numpyclient_has_evaluate
-from .numpy_client import has_fit as numpyclient_has_fit
-from .numpy_client import has_get_parameters as numpyclient_has_get_parameters
-from .numpy_client import has_get_properties as numpyclient_has_get_properties
-
-EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_FIT = """
-NumPyClient.fit did not return a tuple with 3 elements.
-The returned values should have the following type signature:
-
-    Tuple[NDArrays, int, Dict[str, Scalar]]
-
-Example
--------
-
-    model.get_weights(), 10, {"accuracy": 0.95}
-
-"""
-
-EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_EVALUATE = """
-NumPyClient.evaluate did not return a tuple with 3 elements.
-The returned values should have the following type signature:
-
-    Tuple[float, int, Dict[str, Scalar]]
-
-Example
--------
-
-    0.5, 10, {"accuracy": 0.95}
-
-"""
+from .numpy_client import NumPyClient, _wrap_numpy_client
 
 
 def to_client(client_like: ClientLike) -> Client:
@@ -71,100 +25,3 @@ def to_client(client_like: ClientLike) -> Client:
     if isinstance(client_like, NumPyClient):
         return _wrap_numpy_client(client=client_like)
     return client_like
-
-
-def _constructor(self: Client, numpy_client: NumPyClient) -> None:
-    self.numpy_client = numpy_client  # type: ignore
-
-
-def _get_properties(self: Client, ins: GetPropertiesIns) -> GetPropertiesRes:
-    """Return the current client properties."""
-    properties = self.numpy_client.get_properties(config=ins.config)  # type: ignore
-    return GetPropertiesRes(
-        status=Status(code=Code.OK, message="Success"),
-        properties=properties,
-    )
-
-
-def _get_parameters(self: Client, ins: GetParametersIns) -> GetParametersRes:
-    """Return the current local model parameters."""
-    parameters = self.numpy_client.get_parameters(config=ins.config)  # type: ignore
-    parameters_proto = ndarrays_to_parameters(parameters)
-    return GetParametersRes(
-        status=Status(code=Code.OK, message="Success"), parameters=parameters_proto
-    )
-
-
-def _fit(self: Client, ins: FitIns) -> FitRes:
-    """Refine the provided parameters using the locally held dataset."""
-    # Deconstruct FitIns
-    parameters: NDArrays = parameters_to_ndarrays(ins.parameters)
-
-    # Train
-    results = self.numpy_client.fit(parameters, ins.config)  # type: ignore
-    if not (
-        len(results) == 3
-        and isinstance(results[0], list)
-        and isinstance(results[1], int)
-        and isinstance(results[2], dict)
-    ):
-        raise Exception(EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_FIT)
-
-    # Return FitRes
-    parameters_prime, num_examples, metrics = results
-    parameters_prime_proto = ndarrays_to_parameters(parameters_prime)
-    return FitRes(
-        status=Status(code=Code.OK, message="Success"),
-        parameters=parameters_prime_proto,
-        num_examples=num_examples,
-        metrics=metrics,
-    )
-
-
-def _evaluate(self: Client, ins: EvaluateIns) -> EvaluateRes:
-    """Evaluate the provided parameters using the locally held dataset."""
-    parameters: NDArrays = parameters_to_ndarrays(ins.parameters)
-
-    results = self.numpy_client.evaluate(parameters, ins.config)  # type: ignore
-    if not (
-        len(results) == 3
-        and isinstance(results[0], float)
-        and isinstance(results[1], int)
-        and isinstance(results[2], dict)
-    ):
-        raise Exception(EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_EVALUATE)
-
-    # Return EvaluateRes
-    loss, num_examples, metrics = results
-    return EvaluateRes(
-        status=Status(code=Code.OK, message="Success"),
-        loss=loss,
-        num_examples=num_examples,
-        metrics=metrics,
-    )
-
-
-def _wrap_numpy_client(client: NumPyClient) -> Client:
-    member_dict: Dict[str, Callable] = {  # type: ignore
-        "__init__": _constructor,
-    }
-
-    # Add wrapper type methods (if overridden)
-
-    if numpyclient_has_get_properties(client=client):
-        member_dict["get_properties"] = _get_properties
-
-    if numpyclient_has_get_parameters(client=client):
-        member_dict["get_parameters"] = _get_parameters
-
-    if numpyclient_has_fit(client=client):
-        member_dict["fit"] = _fit
-
-    if numpyclient_has_evaluate(client=client):
-        member_dict["evaluate"] = _evaluate
-
-    # Create wrapper class
-    wrapper_class = type("NumPyClientWrapper", (Client,), member_dict)
-
-    # Create and return an instance of the newly created class
-    return wrapper_class(numpy_client=client)  # type: ignore

From fb0206390137eaf35197c06c6dfc32bfa0e4311c Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 10 Oct 2023 13:23:51 +0100
Subject: [PATCH 49/60] Let `start_simulation` crash on exception (#2447)

---
 doc/source/ref-changelog.md   |  2 +-
 src/py/flwr/simulation/app.py | 14 ++++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 8ee97a955fea..341c1d8dad77 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -24,7 +24,7 @@
 
 - **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))
 
-- **General updates to the simulation engine** ([#2331](https://github.com/adap/flower/pull/2331), [#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))
 
 - **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), [#2493](https://github.com/adap/flower/pull/2493))
 
diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py
index bfa72debaae4..6e169493f5c8 100644
--- a/src/py/flwr/simulation/app.py
+++ b/src/py/flwr/simulation/app.py
@@ -69,7 +69,8 @@
 """
 
 
-def start_simulation(  # pylint: disable=too-many-arguments,too-many-statements
+# pylint: disable=too-many-arguments,too-many-statements,too-many-branches
+def start_simulation(
     *,
     client_fn: ClientFn,
     num_clients: Optional[int] = None,
@@ -300,6 +301,7 @@ def update_resources(f_stop: threading.Event) -> None:
         )
         initialized_server.client_manager().register(client=client_proxy)
 
+    hist = History()
     # pylint: disable=broad-except
     try:
         # Start training
@@ -327,11 +329,11 @@ def update_resources(f_stop: threading.Event) -> None:
             client_resources,
             client_resources,
         )
-        hist = History()
+        raise RuntimeError("Simulation crashed.") from ex
 
-    # Stop time monitoring resources in cluster
-    f_stop.set()
-
-    event(EventType.START_SIMULATION_LEAVE)
+    finally:
+        # Stop time monitoring resources in cluster
+        f_stop.set()
+        event(EventType.START_SIMULATION_LEAVE)
 
     return hist

From cee501347c6f4a6d8ca8b8564940eb23199c3d7c Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 10 Oct 2023 15:14:58 +0200
Subject: [PATCH 50/60] Add new doc page for contributing to translations
 (#2410)

---
 doc/source/_static/weblate_interface.png      | Bin 0 -> 716687 bytes
 doc/source/_static/weblate_status.png         | Bin 0 -> 118948 bytes
 ...ibutor-how-to-improve-docs-translation.rst |  72 ++++++++++++++++++
 doc/source/index.rst                          |   1 +
 4 files changed, 73 insertions(+)
 create mode 100644 doc/source/_static/weblate_interface.png
 create mode 100644 doc/source/_static/weblate_status.png
 create mode 100644 doc/source/contributor-how-to-improve-docs-translation.rst

diff --git a/doc/source/_static/weblate_interface.png b/doc/source/_static/weblate_interface.png
new file mode 100644
index 0000000000000000000000000000000000000000..1acf00c81024bdb7560044fe189abb5e31de9dee
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diff --git a/doc/source/contributor-how-to-improve-docs-translation.rst b/doc/source/contributor-how-to-improve-docs-translation.rst
new file mode 100644
index 000000000000..e8edcee352a2
--- /dev/null
+++ b/doc/source/contributor-how-to-improve-docs-translation.rst
@@ -0,0 +1,72 @@
+Improve docs translations
+=========================
+
+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.
+
+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.
+
+Contributing to existing languages
+----------------------------------
+
+.. youtube:: 10_Xfy5BOfQ
+   :width: 100%
+
+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>`_.
+
+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.
+
+Once you have selected the language you want to contribute to, you should see a
+similar interface to this:
+
+   .. image:: _static/weblate_status.png
+
+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.
+
+This is what the interface looks like:
+
+  .. image:: _static/weblate_interface.png
+
+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).
+
+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.
+
+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.
+
+For more information about translating using Weblate, you can check out this
+`in-depth guide <https://docs.weblate.org/en/latest/user/translating.html>`_.
+
+Adding new languages
+--------------------
+
+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>`_.
+
diff --git a/doc/source/index.rst b/doc/source/index.rst
index 4ac99cc24c09..55e81dbaa098 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -159,6 +159,7 @@ The Flower community welcomes contributions. The following docs are intended to
    contributor-how-to-create-new-messages
    contributor-how-to-write-documentation
    contributor-how-to-release-flower
+   contributor-how-to-improve-docs-translation
 
 .. toctree::
    :maxdepth: 1

From 4f9ce5c642d5a85dab5cc5a5e9133ed84c826d72 Mon Sep 17 00:00:00 2001
From: Alessio Mora <43724325+alessiomora@users.noreply.github.com>
Date: Tue, 10 Oct 2023 16:01:03 +0200
Subject: [PATCH 51/60] Add FedMLB baseline (#2340)

* First FedMLB working with partial results.

* Flwr 1.5

* Update main.py

* Changed requirement to tf==2.12.0

* Implmented stop and restart functionality for the training. Saving/loading state.
Implemented a custom server with MyServer in server.py.
Changed main.py accordingly.
Updated README.me.

* An exemplary bash script to break a simulation in parts.

* Added configurations to manage start and stop from config.

* Added utility to load and save a dictionary to file with pickle.

* Added results for table 1b.

* Added formulation

* Added formulation corrected

* Added formulation corrected

* Added formulation corrected

* Added formulation corrected

* Added formulation corrected

* Added formulation corrected

* Added description of special configs.

* Added charts

* Added charts README.md

* Added charts README.md

* Added charts README.md

* Added charts png

* Added charts png

* Updated charts png

* Added tiny-imagenet charts

* Added tiny-imagenet charts

* Add a simple bash script to divide the simulations in batch of rounds.

* Added tensorboard dev reference for results.

* Added tensorboard dev reference for results.

* Corrected README.md

* Update baselines/FedMLB/pyproject.toml

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/FedMLB/README.md with paper link

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/FedMLB/pyproject.toml

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Updated pyproject.toml as in https://github.com/adap/flower/blob/main/baselines/baseline_template/pyproject.toml

* Added verbose=0 to client.fit

* Moved charts for README.md to new folder _static

* Added support to set custom batch_size overriding local_updates parameter

* Docs for simulation_manager.sh

* Corrected README.md

* Added docstrings

* Added results K=100

* Added results K=100 FedAvg

* Deleted redundant client_ids

* Deleted redundant client_ids parameter for start_simulation

* Added docstring

* Fixed following output of `./dev/format-baseline.sh`

* Fixed following output of `./dev/format-baseline.sh`

* Corrected warnings from pylint

* Added results

* Fixed warnings pylint

* Added type hinting

* Renamed module to fedmlb.

* Renamed module to fedmlb.

* Updated README.md

* Updated README.md

* Updated reference to fedmlb

* Added results FedAvg+KD K=100

* Updated charts in README.md

* Updated charts in README.md

* Fixed formatting

* Update baselines/fedmlb/fedmlb/dataset.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/fedmlb/fedmlb/dataset.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/fedmlb/fedmlb/fedmlb_model.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/fedmlb/fedmlb/models.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/fedmlb/fedmlb/models.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/fedmlb/fedmlb/models.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/fedmlb/fedmlb/fedavg_kd_model.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/fedmlb/fedmlb/main.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Corrected README.md

* Updated python version in README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Updated dataset_preparation.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Updated README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Updated README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Updated python version range in pyproject.toml

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Removed warning in README for TinyImagenet.

* Updated README.md

* Updated tables format in README.md

* Updated img command in README.md

* Removed unused files.

* Added description and authors.

* Fixed formatting.

* Slightly fixing formatting

* Fixed default config

* Changed reference to results from tensorboard.dev to google drive

---------

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
Co-authored-by: Charles Beauville <charles@flower.dev>
---
 baselines/fedmlb/LICENSE                      | 202 ++++++
 baselines/fedmlb/README.md                    | 603 ++++++++++++++++++
 ...ison_cifar100_dirichlet_0.3_clients100.png | Bin 0 -> 53690 bytes
 ...cifar100_dirichlet_0.3_clients100_K100.png | Bin 0 -> 56156 bytes
 ...ison_cifar100_dirichlet_0.3_clients500.png | Bin 0 -> 44631 bytes
 ...tiny-imagenet_dirichlet_0.3_clients100.png | Bin 0 -> 44979 bytes
 ...tiny-imagenet_dirichlet_0.3_clients500.png | Bin 0 -> 47590 bytes
 baselines/fedmlb/fedmlb/__init__.py           |   1 +
 baselines/fedmlb/fedmlb/client.py             |  62 ++
 .../balanced/dirichlet0.3_clients100.txt      |   1 +
 .../balanced/dirichlet0.3_clients500.txt      |   1 +
 .../balanced/dirichlet0.6_clients100.txt      |   1 +
 .../balanced/dirichlet0.6_clients500.txt      |   1 +
 .../balanced/dirichlet0.3_clients100.txt      |   1 +
 .../balanced/dirichlet0.3_clients500.txt      |   1 +
 .../balanced/dirichlet0.6_clients100.txt      |   1 +
 .../balanced/dirichlet0.6_clients500.txt      |   1 +
 baselines/fedmlb/fedmlb/conf/base.yaml        |  39 ++
 baselines/fedmlb/fedmlb/dataset.py            | 185 ++++++
 .../fedmlb/fedmlb/dataset_preparation.py      | 269 ++++++++
 baselines/fedmlb/fedmlb/fedavg_kd_model.py    |  80 +++
 baselines/fedmlb/fedmlb/fedmlb_model.py       | 100 +++
 baselines/fedmlb/fedmlb/main.py               | 415 ++++++++++++
 baselines/fedmlb/fedmlb/models.py             | 434 +++++++++++++
 baselines/fedmlb/fedmlb/server.py             | 119 ++++
 baselines/fedmlb/fedmlb/simulation_manager.sh |  10 +
 baselines/fedmlb/fedmlb/strategy.py           |   1 +
 baselines/fedmlb/fedmlb/utils.py              | 107 ++++
 baselines/fedmlb/pyproject.toml               | 140 ++++
 29 files changed, 2775 insertions(+)
 create mode 100644 baselines/fedmlb/LICENSE
 create mode 100644 baselines/fedmlb/README.md
 create mode 100644 baselines/fedmlb/_static/accuracy_comparison_cifar100_dirichlet_0.3_clients100.png
 create mode 100644 baselines/fedmlb/_static/accuracy_comparison_cifar100_dirichlet_0.3_clients100_K100.png
 create mode 100644 baselines/fedmlb/_static/accuracy_comparison_cifar100_dirichlet_0.3_clients500.png
 create mode 100644 baselines/fedmlb/_static/accuracy_comparison_tiny-imagenet_dirichlet_0.3_clients100.png
 create mode 100644 baselines/fedmlb/_static/accuracy_comparison_tiny-imagenet_dirichlet_0.3_clients500.png
 create mode 100644 baselines/fedmlb/fedmlb/__init__.py
 create mode 100644 baselines/fedmlb/fedmlb/client.py
 create mode 100644 baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.3_clients100.txt
 create mode 100644 baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.3_clients500.txt
 create mode 100644 baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.6_clients100.txt
 create mode 100644 baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.6_clients500.txt
 create mode 100644 baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.3_clients100.txt
 create mode 100644 baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.3_clients500.txt
 create mode 100644 baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.6_clients100.txt
 create mode 100644 baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.6_clients500.txt
 create mode 100644 baselines/fedmlb/fedmlb/conf/base.yaml
 create mode 100644 baselines/fedmlb/fedmlb/dataset.py
 create mode 100644 baselines/fedmlb/fedmlb/dataset_preparation.py
 create mode 100644 baselines/fedmlb/fedmlb/fedavg_kd_model.py
 create mode 100644 baselines/fedmlb/fedmlb/fedmlb_model.py
 create mode 100644 baselines/fedmlb/fedmlb/main.py
 create mode 100644 baselines/fedmlb/fedmlb/models.py
 create mode 100644 baselines/fedmlb/fedmlb/server.py
 create mode 100644 baselines/fedmlb/fedmlb/simulation_manager.sh
 create mode 100644 baselines/fedmlb/fedmlb/strategy.py
 create mode 100644 baselines/fedmlb/fedmlb/utils.py
 create mode 100644 baselines/fedmlb/pyproject.toml

diff --git a/baselines/fedmlb/LICENSE b/baselines/fedmlb/LICENSE
new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/fedmlb/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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diff --git a/baselines/fedmlb/README.md b/baselines/fedmlb/README.md
new file mode 100644
index 000000000000..f2816637221b
--- /dev/null
+++ b/baselines/fedmlb/README.md
@@ -0,0 +1,603 @@
+---
+title: Multi-Level Branched Regularization for Federated Learning
+url: https://proceedings.mlr.press/v162/kim22a.html
+labels: [data heterogeneity, knowledge distillation, image classification] 
+dataset: [cifar100, tiny-imagenet] 
+---
+
+# *_FedMLB_*
+
+> 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:**** [proceedings.mlr.press/v162/kim22a.html](https://proceedings.mlr.press/v162/kim22a.html)
+
+****Authors:**** Jinkyu Kim, Geeho Kim, Bohyung Han
+
+****Abstract:**** *_A critical challenge of federated learning is data
+heterogeneity and imbalance across clients, which
+leads to inconsistency between local networks and
+unstable convergence of global models. To alleviate
+the limitations, we propose a novel architectural
+regularization technique that constructs
+multiple auxiliary branches in each local model by
+grafting local and global subnetworks at several
+different levels and that learns the representations
+of the main pathway in the local model congruent
+to the auxiliary hybrid pathways via online
+knowledge distillation. The proposed technique is
+effective to robustify the global model even in the
+non-iid setting and is applicable to various federated
+learning frameworks conveniently without
+incurring extra communication costs. We perform
+comprehensive empirical studies and demonstrate
+remarkable performance gains in terms of accuracy
+and efficiency compared to existing methods.
+The source code is available in our project page._*
+
+
+## About this baseline
+
+****What’s implemented:**** The code in this directory reproduces the results for FedMLB, FedAvg, and FedAvg+KD.
+The reproduced results use the CIFAR-100 dataset or the TinyImagenet dataset. Four settings are available for both
+the datasets,
+1. Moderate-scale with Dir(0.3), 100 clients, 5% participation, balanced dataset.
+2. Large-scale experiments with Dir(0.3), 500 clients, 2% participation rate, balanced dataset.
+3. Moderate-scale with Dir(0.6), 100 clients, 5% participation rate, balanced dataset.
+4. Large-scale experiments with Dir(0.6), 500 clients, 2% participation rate, balanced dataset.
+
+****Datasets:**** CIFAR-100, Tiny-ImageNet.
+
+****Hardware Setup:**** The code in this repository has been tested on a Linux machine with 64GB RAM. 
+Be aware that in the default config the memory usage can exceed 10GB.
+
+****Contributors:**** Alessio Mora (University of Bologna, PhD, alessio.mora@unibo.it).
+
+## Experimental Setup
+
+****Task:**** Image classification
+
+****Model:**** ResNet-18.
+
+****Dataset:**** Four settings are available for CIFAR-100,
+1. Moderate-scale with Dir(0.3), 100 clients, 5% participation, balanced dataset (500 examples per client).
+2. Large-scale experiments with Dir(0.3), 500 clients, 2% participation rate, balanced dataset (100 examples per client).
+3. Moderate-scale with Dir(0.6), 100 clients, 5% participation rate, balanced dataset (500 examples per client).
+4. Large-scale experiments with Dir(0.6), 500 clients, 2% participation rate, balanced dataset (100 examples per client).
+
+****Dataset:**** Four settings are available for Tiny-Imagenet,
+1. Moderate-scale with Dir(0.3), 100 clients, 5% participation, balanced dataset (1000 examples per client).
+2. Large-scale experiments with Dir(0.3), 500 clients, 2% participation rate, balanced dataset (200 examples per client).
+3. Moderate-scale with Dir(0.6), 100 clients, 5% participation rate, balanced dataset (1000 examples per client).
+4. Large-scale experiments with Dir(0.6), 500 clients, 2% participation rate, balanced dataset (200 examples per client).
+
+****Training Hyperparameters:**** 
+
+| Hyperparameter  | Description | Default Value |
+| ------------- | ------------- | ------------- |
+| client optimizer  | Local optimizer. |  SGD|
+| client learning rate  |  |  0.1 |
+| learning rate decay  | Exponential decay rate for clients' learning rate. |  0.998 |
+| server optimizer  | Server optimizer. (SGD with lr=1 is equivalent to applying updated by sum). |  SGD|
+| server learning rate  |  |  1.0 |
+| clip norm  | Clip norm during local training. | 10.0 |
+| weight decay  | Weight decay during local training. |  1e-3 |
+| $\lambda_1$  | Used in FedMLB. See Eq. 8 in the original paper. It weights the impact of hybrid cross-entropy loss.|  1.0 |
+| $\lambda_2$  | Used in FedMLB. See Eq. 8 in the original paper. It weights the impact of hybrid KL loss.| 1.0 |
+| temperature  | Regulates the smoothness of local and global predictions in distillation. | 1.0 |
+| $\gamma$   | Used in FedAvg+KD. It weights the impact of local-global distillation on local training.| 0.2 |
+
+## Environment Setup
+By default, Poetry will use the Python version in your system. 
+In some settings, you might want to specify a particular version of Python 
+to use inside your Poetry environment. You can do so with `pyenv`. 
+Check the documentation for the different ways of installing `pyenv`,
+but one easy way is using the automatic installer:
+
+```bash
+curl https://pyenv.run | bash
+```
+You can then install any Python version with `pyenv install <python-version>`
+(e.g. `pyenv install 3.9.17`) and set that version as the one to be used. 
+```bash
+# cd to your FedMLB directory (i.e. where the `pyproject.toml` is)
+pyenv install 3.10.6
+
+pyenv local 3.10.6
+
+# set that version for poetry
+poetry env use 3.10.6
+```
+To build the Python environment as specified in the `pyproject.toml`, use the following commands:
+```bash
+# cd to your FedMLB directory (i.e. where the `pyproject.toml` is)
+
+# install the base Poetry environment
+poetry install
+
+# activate the environment
+poetry shell
+```
+## Running the Experiments
+Ensure you have activated your Poetry environment (execute `poetry shell` from
+this directory).
+
+### Generating clients' dataset
+First (and just the first time), the data partitions of clients must be generated.
+
+#### CIFAR-100 clients' dataset generation
+
+To generate the partitions for the CIFAR-100 settings, i.e.:
+1. Moderate-scale with Dir(0.3), 100 clients, balanced dataset (500 examples per client) -- **default config**.
+2. Large-scale experiments with Dir(0.3), 500 clients, balanced dataset (100 examples per client);
+3. Moderate-scale with Dir(0.6), 100 clients, balanced dataset (500 examples per client);
+4. Large-scale experiments with Dir(0.6), 500 clients, balanced dataset (100 examples per client),
+
+use the following commands:
+```bash
+# this will run using the default settings in the `conf/base.yaml`
+# and will generate the setting for 1. (see above)
+python -m fedmlb.dataset_preparation 
+
+# this will generate the setting for 2. (see above)
+python -m fedmlb.dataset_preparation dataset_config.alpha_dirichlet=0.3 total_clients=500
+
+# this will generate the setting for 3. (see above)
+python -m fedmlb.dataset_preparation dataset_config.alpha_dirichlet=0.6 
+
+# this will generate the setting for 4. (see above)
+python -m fedmlb.dataset_preparation dataset_config.alpha_dirichlet=0.6 total_clients=500
+```
+Note that, to reproduce those settings, we leverage the `.txt` files
+contained in the `client_data` folder in this project. Such files store
+the specific id of examples in the dataset that are associated with a specific client. 
+For example, the file `client_data/cifar100/balanced/dirichlet0.3_clients100.txt` contains the
+examples for the default setting of this repository. 
+Note that those files are provided by the authors themselves
+on the [paper's GitHub](https://github.com/jinkyu032/FedMLB/tree/main/client_data).
+
+#### Tiny-ImageNet clients' dataset generation
+
+> Note: To generate the clients' dataset for the Tiny-Imagenet dataset, the original dataset should be downloaded in advance.\
+> It can be downloaded at http://cs231n.stanford.edu/tiny-imagenet-200.zip. Unzip the folder. \
+> Note: This code supposes to find the folder at the path `/{YOUR_LOCAL_PATH_TO_THE_BASELINE}/fedmlb/tiny-imagenet-200`.
+
+To generate the partitions for the Tiny-ImageNet settings, i.e.:
+1. Moderate-scale with Dir(0.3), 100 clients, balanced dataset (1000 examples per client).
+2. Large-scale experiments with Dir(0.3), 500 clients, balanced dataset (200 examples per client).
+3. Moderate-scale with Dir(0.6), 100 clients, balanced dataset (1000 examples per client).
+4. Large-scale experiments with Dir(0.6), 500 clients, balanced dataset (200 examples per client).
+
+use the following commands:
+```bash
+# commands to generate clients' dataset partitions with Tiny-imagenet
+# this will generate the setting for 1. (see above)
+python -m fedmlb.dataset_preparation dataset_config.dataset="tiny-imagenet" 
+
+# this will generate the setting for 2. (see above)
+python -m fedmlb.dataset_preparation dataset_config.dataset="tiny-imagenet" total_clients=500
+
+# this will generate the setting for 3. (see above)
+python -m fedmlb.dataset_preparation dataset_config.dataset="tiny-imagenet" dataset_config.alpha_dirichlet=0.6 
+
+# this will generate the setting for 4. (see above)
+python -m fedmlb.dataset_preparation dataset_config.dataset="tiny-imagenet" dataset_config.alpha_dirichlet=0.6 total_clients=500
+```
+
+### Using GPUs
+The code in this repository relies on TF library.
+To make the simulations run on GPUs use the option `client_resources.num_cpus={PER_CLIENT_FRACTION_OF_GPU_MEMORY}`.
+The default is `num_gpus=0.0`. 
+For example, the following command will run on CPU only. 
+```bash
+python -m fedmlb.main # `client_resources.num_gpus=0.0` default
+
+# If for instance you want to allow for two clients running on the same GPU
+python -m fedmlb.main client_resources.num_gpus=0.5
+```
+
+> :warning:
+Ensure that TensorFlow is configured to use GPUs.
+
+> To use TensorFlow with GPU, CUDA and cuDNN
+> have to be installed.
+> Check the compatibility of versions here:
+> https://www.tensorflow.org/install/source#gpu.
+> For TF==2.12.0 (as required for this baseline implementation)
+> CUDA==11.8 and cuDNN==8.6 are required. 
+> 
+
+
+### Run simulations and reproduce results
+After having generated the setting, simulations can be run.
+ 
+#### CIFAR-100
+The default configuration for `FedMLB.main` uses (1.) for CIFAR-100, and can be run with the following:
+
+```bash
+python -m fedmlb.main # this will run using the default settings in the `conf/base.yaml`
+```
+
+You can override settings directly from the command line in this way:
+
+```bash
+python -m fedmlb.main clients_per_round=10 # this will run using 10 clients per round instead of 5 clients as the default config 
+
+# this will select the dataset partitioned with 0.6 concentration paramater instead of 0.3 as the default config
+python -m fedmlb.main dataset_config.alpha_dirichlet=0.6
+```
+
+To run using FedAvg:
+```bash
+# this will use the regular FedAvg local training
+python -m fedmlb.main algorithm="FedAvg"
+```
+
+To run experiments with all the configurations of CIFAR-100, use the followings:
+
+```bash
+# this will use the setting for 1. (default)
+python -m fedmlb.main
+ 
+# this will use the setting for 2. (see above)
+python -m fedmlb.main total_clients=500 clients_per_round=10 
+
+# this will use the setting for 3. (see above)
+python -m fedmlb.main dataset_config.alpha_dirichlet=0.6  
+
+# this will use the setting for 4. (see above)
+python -m fedmlb.main dataset_config.alpha_dirichlet=0.6 total_clients=500 clients_per_round=10
+```
+
+#### Tiny-Imagenet
+For Tiny-ImageNet, as in the orginal paper, batch size of local updates should be set 
+to 100 in settings with 100 clients and to 20 in settings with 500 clients;
+this is equal to set the amount of local_updates to 50 (as the default) -- 
+so no change to batch size is required --, in fact
+
+local_updates = num_of_local_examples*local_epochs / batch_size
+
+To run experiments with all the configurations of Tiny-ImageNet, use the followings:
+
+```bash
+# this will use the setting for 1. (see above)
+python -m fedmlb.main dataset_config.dataset="tiny-imagenet" 
+
+# this will use the setting for 2. (see above)
+python -m fedmlb.main dataset_config.dataset="tiny-imagenet" total_clients=500 clients_per_round=10
+
+# this will use the setting for 3. (see above)
+python -m fedmlb.main dataset_config.dataset="tiny-imagenet" dataset_config.alpha_dirichlet=0.6  
+
+# this will use the setting for 4. (see above)
+python -m fedmlb.main dataset_config.dataset="tiny-imagenet" dataset_config.alpha_dirichlet=0.6 total_clients=500 clients_per_round=10
+```
+
+#### Setting a custom local batch size value
+In the original paper, authors specify the amount of local updates (default to 50), 
+and the amount of local epochs (default to 5), so that the batch size corresponds to:
+
+`local_batch_size = round(local_examples * local_epochs / local_updates)`
+
+This is the default configuration also in this repository.
+
+To override this behaviour, and set a specific local batch size (local epochs will still 
+be valid), use the following argument when launching the simulation.
+
+```
+# this will set the local batch size to 64
+python -m fedmlb.main batch_size=64
+```
+
+Alternatively, it can be done by directly modifying the `base.yaml` config file by changing the value
+of `batch_size`, as follows:
+```
+# base.yaml
+...
+batch_size=64 # default to null
+...
+``` 
+
+
+### Splitting a Simulation in a bunch of Shorter Simulations
+`simulation_manager.sh` contains a simple bash script to
+divide a simulation in a bunch of shorter simulations.
+For example, it can divide a simulation of 1000 rounds 
+(`total_round`) in
+50 consecutive simulations of 20 rounds each (`rounds_per_run`).
+By the automatic retrieve of last checkpoint, 
+the current simulation
+restarts from where the previous one stopped and saves a
+checkpoint at the end.
+
+To be able to run the script, first give it the execution permission
+(for Linux machines) with:
+``` 
+chmod +x ./simulation_manager.sh
+``` 
+Then launch it with:
+``` 
+./simulation_manager.sh
+``` 
+## Expected Results
+This repository can reproduce the results for 3 baselines used in the experimental part
+of the original paper: FedMLB, FedAvg, FedAvg+KD.
+
+The following tables compares results produced via the code in this repository
+with the results reported in the paper.
+Results from the paper are reported in brackets. 
+Note that (as in the original paper),
+the accuracy at the target round is based on the exponential moving average with the momentum parameter
+0.9. Tensorboard allows to easily visualize/calculate the moving average of a run.
+
+All the results reported in this repository can be also downloaded 
+from [this link](https://drive.google.com/file/d/1XeTnZfnW1ncaQxPdvDeShXq2dnRR5Njj/view?usp=sharing) 
+(`.csv` file, 2MB).
+
+### Table 1a and Table 2
+
+The results of Table 1a in the paper (for FedAvg and FedMLB) refers
+to CIFAR-100 (Figure 3) and Tiny-ImageNet (Figure 7a) with 
+the setting (1.) Moderate-scale with Dir(0.3), 100 clients, 5% participation rate. 
+Results for FedAvg+KD are from Table 2 in the paper.
+
+To reproduce the results run the following:
+
+```bash
+# this will produce six consecutive runs
+python -m fedmlb.main --multirun dataset_config.dataset="cifar100","tiny-imagenet" algorithm="FedMLB","FedAvg","FedAvg+KD" 
+```
+
+#### CIFAR-100 and Tiny-ImageNet, Dir(0.3), 100 clients, 5% participation.
+
+<table>
+  <tr>
+    <td></td>
+    <td style="text-align: center", colspan="4">CIFAR-100</td>
+    <td style="text-align: center", colspan="4">Tiny-ImageNet</td>
+  </tr>
+  <tr>
+    <td></td>
+    <td colspan="2">Accuracy @500R</td>
+    <td colspan="2">Accuracy @1000R</td>
+    <td colspan="2">Accuracy @500R</td>
+    <td colspan="2">Accuracy @1000R</td>
+  </tr>
+  <tr>
+    <td>Method</td>
+    <td>Paper</td>
+    <td>This repo</td>
+    <td>Paper</td>
+    <td>This repo</td>
+    <td>Paper</td>
+    <td>This repo</td>
+    <td>Paper</td>
+    <td>This repo</td>
+  </tr>
+  <tr>
+    <td>FedAvg</td>
+    <td>41.88</td>
+    <td>44.52</td>
+    <td>47.83</td>
+    <td>49.15</td>
+    <td>33.94</td>
+    <td>33.39</td>
+    <td>35.42</td>
+    <td>35.78</td>
+  </tr>
+  <tr>
+    <td>FedAvg+KD</td>
+    <td>42.99</td>
+    <td>46.03</td>
+    <td>49.17</td>
+    <td>50.54</td>
+    <td></td>
+    <td></td>
+    <td></td>
+    <td></td>
+  </tr>
+  <tr>
+    <td>FedMLB</td>
+    <td>47.39</td>
+    <td>51.11</td>
+    <td>54.58</td>
+    <td>57.33</td>
+    <td>37.20</td>
+    <td>35.42</td>
+    <td>40.16</td>
+    <td>39.14</td>
+  </tr>
+</table>
+
+<img alt="cifar100, 100 clients, 5% participation rate, alpha = 0.3" src="_static/accuracy_comparison_cifar100_dirichlet_0.3_clients100.png" width="400"/> <img alt="tiny-imagenet, 100 clients, 5% participation rate, alpha = 0.3" src="_static/accuracy_comparison_tiny-imagenet_dirichlet_0.3_clients100.png" width="400"/>
+
+### Table 1b and Table 2
+
+The results of Table 1b in the paper (for FedAvg and FedMLB) refers
+to CIFAR-100 (Figure 3) and Tiny-ImageNet (Figure 7b) with 
+the setting (2.) Large-scale experiments with Dir(0.3), 500 clients, 
+2% participation rate.
+
+To reproduce the results run the following:
+```bash
+python -m fedmlb.main --multirun dataset_config.dataset="cifar100","tiny-imagenet" algorithm="FedMLB","FedAvg" total_clients=500 clients_per_round=10
+```
+#### CIFAR-100 and Tiny-ImageNet, Dir(0.3), 500 clients, 2% participation.
+
+<table>
+  <tr>
+    <td></td>
+    <td style="text-align: center", colspan="4">CIFAR-100</td>
+    <td style="text-align: center", colspan="4">Tiny-ImageNet</td>
+  </tr>
+  <tr>
+    <td></td>
+    <td colspan="2">Accuracy @500R</td>
+    <td colspan="2">Accuracy @1000R</td>
+    <td colspan="2">Accuracy @500R</td>
+    <td colspan="2">Accuracy @1000R</td>
+  </tr>
+  <tr>
+    <td>Method</td>
+    <td>Paper</td>
+    <td>This repo</td>
+    <td>Paper</td>
+    <td>This repo</td>
+    <td>Paper</td>
+    <td>This repo</td>
+    <td>Paper</td>
+    <td>This repo</td>
+  </tr>
+  <tr>
+    <td>FedAvg</td>
+    <td>29.87</td>
+    <td>32.54</td>
+    <td>37.48</td>
+    <td>38.75</td>
+    <td>23.63</td>
+    <td>22.62</td>
+    <td>29.48</td>
+    <td>28.37</td>
+  </tr>
+  <tr>
+    <td>FedMLB</td>
+    <td>32.03</td>
+    <td>36.63</td>
+    <td>42.61</td>
+    <td>46.65</td>
+    <td>28.39</td>
+    <td>26.84</td>
+    <td>33.67</td>
+    <td>33.13</td>
+  </tr>
+</table>
+
+<img alt="cifar100, 500 clients, 2% participation rate, alpha = 0.3" src="_static/accuracy_comparison_cifar100_dirichlet_0.3_clients500.png" width="400"/> <img alt="tiny-imagenet, 500 clients, 2% participation rate, alpha = 0.3" src="_static/accuracy_comparison_tiny-imagenet_dirichlet_0.3_clients500.png" width="400"/>
+
+### Table 3
+
+To reproduce results reported in Table 3 of the paper,
+resulting from _more local iterations_ (K=100 or K=200 in the
+paper, instead of K=50), run the following:
+```bash
+python -m fedmlb.main --multirun algorithm="FedMLB","FedAvg","FedAvg+KD" local_updates=100 # or local_updates=200 
+```
+#### K = 100 (local updates)
+<table>
+  <tr>
+    <td></td>
+    <td colspan="2">Accuracy @500R</td>
+    <td colspan="2">Accuracy @1000R</td>
+  </tr>
+  <tr> 
+    <td>Method</td>
+    <td>Paper</td>
+    <td>This repo</td>
+    <td>Paper</td>
+    <td>This repo</td>
+  </tr>
+  <tr>
+    <td>FedAvg</td>
+    <td>41.92</td>
+    <td>44.02</td>
+    <td>48.15</td>
+    <td>49.05</td>
+  </tr>
+  <tr>
+    <td>FedAvg+KD</td> 
+    <td>42.58</td>
+    <td>45.49</td>
+    <td>49.15</td>
+    <td>50.58</td>
+  </tr>
+  <tr>
+    <td>FedMLB</td> 
+    <td>52.53</td>
+    <td>52.38</td>
+    <td>58.52</td>
+    <td>57.69</td>
+  </tr>
+</table>
+
+<img alt="cifar100, 100 clients, 5% participation rate, alpha = 0.3, K=100" src="_static/accuracy_comparison_cifar100_dirichlet_0.3_clients100_K100.png" width="400"/> 
+
+## Results logging via Tensorboard
+Beside storing results in plain text in the `output` folder, the results are also stored via 
+tensorboard logs.
+
+To launch the tensorboard to monitor results use the following command
+in your activated python environment:
+```bash
+tensorboard --logdir /{YOUR_LOCAL_PATH_TO_THE_BASELINE}/fedmlb/fedmlb/tb_logging/
+```
+The command will output an address for localhost,
+and results can be navigated and visualized via tensorboard GUI 
+by using a browser at that address. 
+
+Tensorboard logs in this baselines are stored in a folder with the following path structure: 
+`fedmlb/fedmlb/tb_logging/{DATASET}/{MODEL}/{METHOD}/K_{LOCAL_UPDATES}/{TOTAL_CLIENTS}_clients/dir_{ALPHA_DIRICHLET}/seed_{RANDOM_SEED}`
+
+For example, for default results with FedAvg, logs will be stored at:
+`fedmlb/fedmlb/tb_logging/cifar100/resnet18/FedAvg/K_50/100_clients/dir_0.3/seed_3`
+ 
+## Additional Detail About This Implementation
+The `models.py` file contains the implementation of the models used in this baseline. A regular ResNet18 is
+implemented for regular FedAvg. However, FedMLB leverages *hybrid paths* to regularize local training,
+with such hybrid paths composed of a series of frozen blocks from the global models (see the paper
+for reference). As in the original implementation, in `models.py` you will find the `ResNet18MLB`
+that is a custom version of the regular ResNet18 containing the support for such hybrid paths (see the
+`call` method of `ResNet18MLB` class).
+
+This baseline implementation is based on TensorFlow and Keras primitives. Note that the custom
+local training of `FedMLB` and `FedAvg+KD` is implemented by subclassing the `tf.keras.Model` class 
+and overriding the `train_step` method (which is called automatically at each batch iteration). The
+custom `tf.keras.Model` are contained in `FedAvgKDModel.py` and `FedMLBModel.py`.
+
+Note that `FedMLB` and `FedAvg+KD` are client-side methods, which do not impact the aggregation phase 
+of regular FedAvg (so this means that the built-in Flower strategy for FedAvg does not require
+modifications). For this reason, there is no relevant modification in `server.py`; the `MyServer` class
+just allows to start the training starting from a round > 1 (useful to stop and restart a training
+saving/loading its state).
+
+To reproduce results, preprocessing of data is of paramount importance. 
+For CIFAR-100, the preprocessing of training images includes normalization,
+random rotation, random crop and random flip of images; test images
+are just normalized. 
+For Tiny-ImageNet, the preprocessing of training images includes normalization,
+random rotation, random crop and random flip of images; test images
+are normalized and center cropped.
+This code uses or defines subclasses of the `tf.keras.layers.Layer` class
+to leverage a series of preprocessing layer to be used in the `.map()`
+primitive of `tf.data.Dataset` (see `dataset.py`).
+
+### Special Configs
+The config file (`base.yaml`) contains some flags to enable features like restarting from
+saved checkpoint, saving a checkpoint at the end of the training, and logging
+memory usage.
+
+- `restart_from_checkpoint: True`
+If True, looks for a checkpoint of that config to restart the training,
+otherwise initializes a new model.
+
+- `save_checkpoint: True`
+If True, saves a checkpoint server model at the end of the training. Checkpoints 
+will be saved at the local path:
+`fedmlb/fedmlb/model_checkpoints/{DATASET}/{MODEL}/{METHOD}/K_{LOCAL_UPDATES}/{TOTAL_CLIENTS}_clients/dir_{ALPHA_DIRICHLET}/seed_{RANDOM_SEED}/checkpoint_R{CURRENT_ROUND}`.
+
+- `logging_memory_usage: False`
+If True, logs memory and GPU's memory usage (need for psutil and nvidia-smi 
+installed).
+
+## Differences with the Original Implementation
+In the original implementation from 
+[paper's GitHub](https://github.com/jinkyu032/FedMLB/blob/main/federated_train.py#L47C12-L47C12)
+CIFAR-100 images are normalized as follows:
+
+`normalize = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))`
+
+In this repository, normalization values for the three channels are slightly different, i.e.:
+
+`tf.keras.layers.Normalization(mean=[0.5071, 0.4865, 0.4409], 
+                                variance=[np.square(0.2673), np.square(0.2564), np.square(0.2762)])`
+
+This may be the reason for the slightly improved results reported in this repo with CIFAR-100 clients 
+with respect to the results in the original paper.
diff --git a/baselines/fedmlb/_static/accuracy_comparison_cifar100_dirichlet_0.3_clients100.png b/baselines/fedmlb/_static/accuracy_comparison_cifar100_dirichlet_0.3_clients100.png
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diff --git a/baselines/fedmlb/fedmlb/__init__.py b/baselines/fedmlb/fedmlb/__init__.py
new file mode 100644
index 000000000000..a5e567b59135
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/fedmlb/fedmlb/client.py b/baselines/fedmlb/fedmlb/client.py
new file mode 100644
index 000000000000..443997f602df
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/client.py
@@ -0,0 +1,62 @@
+"""Define TensorFlow client class by subclassing `flwr.client.NumPyClient`."""
+
+from typing import Dict, Tuple
+
+import flwr as fl
+import tensorflow as tf
+from flwr.common import NDArrays, Scalar
+
+
+class TFClient(fl.client.NumPyClient):
+    """Tensorflow Client implementation."""
+
+    def __init__(
+        self,
+        train_ds: tf.data.Dataset,
+        model: tf.keras.Model,
+        num_examples_train: int,
+        algorithm: str,
+    ):
+        self.model = model
+        self.train_ds = train_ds
+        self.num_examples_train = num_examples_train
+        self.algorithm = algorithm
+
+    def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
+        """Get the local model parameters."""
+        return self.model.get_weights()
+
+    def fit(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[NDArrays, int, Dict]:
+        """Train parameters on the locally held training set."""
+        epochs: int = int(config["local_epochs"])
+        current_round: int = int(config["current_round"])
+        exp_decay: float = float(config["exp_decay"])
+        lr_client_initial: float = float(config["lr_client_initial"])
+        if current_round > 1:
+            lr_client = lr_client_initial * (exp_decay ** (current_round - 1))
+            # During training, update the learning rate as needed
+            tf.keras.backend.set_value(self.model.optimizer.lr, lr_client)
+
+        # Update local model parameters
+        if self.algorithm in ["FedMLB", "FedAvg+KD"]:
+            self.model.local_model.set_weights(parameters)
+            self.model.global_model.set_weights(parameters)
+        elif self.algorithm in ["FedAvg"]:
+            self.model.set_weights(parameters)
+
+        # Get hyperparameters for this round
+        # batch_size: int = config["batch_size"]
+        # the dataset is already batched,
+        # so there is no need to retrieve the batch size
+
+        # in model.fit it is not mandatory to specify
+        # batch_size if the dataset is already batched
+        # as in our case
+        results = self.model.fit(self.train_ds, epochs=epochs, verbose=0)
+
+        parameters_prime = self.model.get_weights()
+        num_examples_train = self.num_examples_train
+
+        return parameters_prime, int(num_examples_train), results.history
diff --git a/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.3_clients100.txt b/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.3_clients100.txt
new file mode 100644
index 000000000000..1621b4db8f4c
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.3_clients100.txt
@@ -0,0 +1 @@
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diff --git a/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.3_clients500.txt b/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.3_clients500.txt
new file mode 100644
index 000000000000..dd8824756077
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.3_clients500.txt
@@ -0,0 +1 @@
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diff --git a/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.6_clients100.txt b/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.6_clients100.txt
new file mode 100644
index 000000000000..0e97345a668d
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.6_clients100.txt
@@ -0,0 +1 @@
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diff --git a/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.6_clients500.txt b/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.6_clients500.txt
new file mode 100644
index 000000000000..ff3fcc418c83
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/client_data/Tiny-ImageNet/balanced/dirichlet0.6_clients500.txt
@@ -0,0 +1 @@
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diff --git a/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.3_clients100.txt b/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.3_clients100.txt
new file mode 100644
index 000000000000..b4df9d0dd500
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.3_clients100.txt
@@ -0,0 +1 @@
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diff --git a/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.3_clients500.txt b/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.3_clients500.txt
new file mode 100644
index 000000000000..c8ea7c4992e7
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.3_clients500.txt
@@ -0,0 +1 @@
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diff --git a/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.6_clients100.txt b/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.6_clients100.txt
new file mode 100644
index 000000000000..f7977b0e4bc2
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.6_clients100.txt
@@ -0,0 +1 @@
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diff --git a/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.6_clients500.txt b/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.6_clients500.txt
new file mode 100644
index 000000000000..c514a11e5456
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/client_data/cifar100/balanced/dirichlet0.6_clients500.txt
@@ -0,0 +1 @@
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46096, 47414, 33335, 8207, 5370, 46313, 22016, 26288, 35093, 5635, 17885, 43890, 1744, 21017, 18015, 39083, 46987, 43461, 35078, 5554, 2496, 28814, 46366, 7737, 46121], 496: [3811, 4231, 47378, 33349, 6193, 41888, 30357, 26306, 38302, 11972, 43924, 42833, 10337, 39865, 34274, 19029, 33556, 9038, 34372, 39263, 31395, 38986, 39114, 1260, 34341, 22473, 34484, 3597, 41782, 12636, 23879, 49010, 31079, 22569, 36916, 25041, 9335, 37861, 34416, 47904, 29140, 17965, 34784, 47563, 5904, 42328, 35566, 13666, 7873, 29561, 43106, 28362, 26065, 47899, 11049, 46270, 43234, 20926, 13658, 38005, 18733, 1678, 28110, 48438, 22934, 28312, 1181, 7395, 46832, 5470, 16706, 6208, 7471, 25026, 12591, 5607, 16496, 46016, 38484, 48242, 36194, 24419, 37754, 48356, 16763, 3176, 14151, 9681, 13130, 632, 2185, 1018, 34434, 4335, 10783, 10499, 43765, 36474, 10135, 11428], 497: [19015, 24275, 22248, 34758, 4981, 27302, 16752, 49658, 41373, 37245, 4122, 6084, 37938, 12135, 48797, 35438, 28045, 43429, 11937, 43767, 43166, 18935, 28845, 40789, 33748, 22156, 12411, 15880, 44632, 8055, 16623, 41091, 20069, 14644, 1266, 12130, 18705, 6079, 37936, 14079, 3114, 17747, 5032, 18977, 5797, 7999, 7954, 40096, 46195, 26181, 32436, 6895, 37984, 36912, 45643, 3786, 16293, 11608, 40958, 3675, 23610, 2201, 16413, 10653, 34674, 19452, 19303, 18922, 22728, 29258, 35199, 42542, 13118, 45616, 26248, 22371, 10579, 630, 8323, 36473, 19765, 1796, 5142, 8944, 33014, 10281, 33307, 12295, 27062, 20494, 30677, 12185, 3521, 43137, 29223, 21233, 13222, 48476, 3498, 1168], 498: [7729, 38879, 49165, 3714, 41466, 37537, 39862, 3297, 11065, 47554, 45700, 22080, 18657, 45447, 3960, 19351, 5043, 33585, 48169, 44976, 13226, 38946, 37284, 39211, 24627, 9981, 25818, 8185, 35494, 29491, 25699, 16323, 15886, 35576, 17669, 38303, 31128, 28636, 31619, 14287, 6660, 14224, 24923, 23647, 48443, 21, 9195, 5090, 26157, 11842, 2202, 13755, 3604, 14330, 38534, 46430, 3070, 11483, 13823, 22825, 14734, 46108, 47967, 10611, 40984, 6752, 37748, 37855, 14749, 34849, 728, 11260, 24499, 30328, 8654, 10087, 43550, 43490, 16302, 49931, 32610, 9432, 5213, 13301, 39730, 12159, 26593, 44300, 17913, 8548, 4619, 19107, 25098, 17859, 20905, 21775, 42840, 20422, 39242, 24231], 499: [19715, 20431, 19612, 37705, 39941, 7570, 19494, 13610, 44141, 44345, 39989, 18469, 31251, 17915, 3219, 32883, 40814, 42966, 20430, 20471, 23322, 9323, 30018, 46259, 33825, 47656, 20990, 30879, 2097, 42187, 17495, 18128, 20145, 10200, 29172, 26010, 3530, 45173, 34333, 13389, 49407, 40842, 44614, 24707, 10398, 32006, 17326, 6419, 6611, 23754, 45662, 21076, 29188, 23780, 45593, 47482, 17358, 41755, 10008, 39802, 2667, 15129, 40500, 21097, 40183, 47084, 20551, 12567, 25693, 34997, 5795, 44509, 22576, 45421, 15275, 11233, 14789, 29871, 37806, 18449, 48767, 44383, 49839, 1653, 32017, 21280, 24762, 10549, 37351, 42130, 46750, 7351, 11761, 33400, 36551, 34844, 24857, 8650, 33369, 1055]}
diff --git a/baselines/fedmlb/fedmlb/conf/base.yaml b/baselines/fedmlb/fedmlb/conf/base.yaml
new file mode 100644
index 000000000000..eec17c56dcc7
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/conf/base.yaml
@@ -0,0 +1,39 @@
+---
+# 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)
+
+algorithm: "FedMLB" # can be "FedAvg" or "FedAvg+KD" or "FedMLB"
+total_clients: 100 # total number of clients
+num_rounds: 1000
+clients_per_round: 5
+local_updates: 50
+batch_size: null # if set to null, it will calculates local_batch_size as round(local_examples * local_epochs / local_updates)
+local_epochs: 5 # number of local epochs
+lr_client: 0.1 # client learning rate
+exp_decay: 0.998
+clipnorm: 10.0
+l2_weight_decay: 1e-3
+lambda_1: 1.0 # it can be >= 0
+lambda_2: 1.0 # it can be >= 0
+random_seed: 23
+starting_round: 1
+restart_from_checkpoint: True # if True, looks for a checkpoint of that config to restart the training
+save_checkpoint: True # if True, saves a checkpoint server model at the end of the training
+logging_memory_usage: False # if True logs memory and GPU's memory usage (need for psutil and nvidia-smi installed)
+
+client_resources:
+  num_cpus: 1.0
+  num_gpus: 0.0
+
+dataset_config:
+  dataset: "cifar100" # can be "cifar100" or "tiny-imagenet"
+  alpha_dirichlet: 0.3 # can be 0.3, 0.6
+
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  fraction_fit: 0.00001
+  fraction_evaluate: 0.0
+  min_fit_clients: ${clients_per_round}
+  min_evaluate_clients: 0
+  min_available_clients: ${total_clients}
diff --git a/baselines/fedmlb/fedmlb/dataset.py b/baselines/fedmlb/fedmlb/dataset.py
new file mode 100644
index 000000000000..290f7600332f
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/dataset.py
@@ -0,0 +1,185 @@
+"""Handle dataset loading and preprocessing utility."""
+import os
+from typing import Union
+
+import numpy as np
+import tensorflow as tf
+
+
+def load_selected_client_statistics(
+    selected_client: int, alpha: float, dataset: str, total_clients: int
+):
+    """Return the amount of local examples for the selected client.
+
+    Clients are referenced with a client_id. Loads a numpy array saved on disk. This
+    could be done directly by doing len(ds.to_list()) but it's more expensive at run
+    time.
+    """
+    path = os.path.join(
+        dataset + "_mlb_dirichlet_train",
+        str(total_clients),
+        str(round(alpha, 2)),
+        "distribution_train.npy",
+    )
+    smpls_loaded = np.load(path)
+    local_examples_all_clients = np.sum(smpls_loaded, axis=1)
+    return local_examples_all_clients[selected_client]
+
+
+# pylint: disable=W0221
+class PaddedRandomCropCustom(tf.keras.layers.Layer):
+    """Custom keras layer to random crop the input image, same as FedMLB paper."""
+
+    def __init__(
+        self, seed: Union[int, None] = None, height: int = 32, width: int = 32, **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.seed = seed
+        self.height = height
+        self.width = width
+
+    def call(self, inputs: tf.Tensor, training: bool = True):
+        """Call the layer on new inputs and returns the outputs as tensors."""
+        if training:
+            inputs = tf.image.resize_with_crop_or_pad(
+                image=inputs,
+                target_height=self.height + 4,
+                target_width=self.width + 4,
+            )
+            inputs = tf.image.random_crop(
+                value=inputs, size=[self.height, self.width, 3], seed=self.seed
+            )
+
+            return inputs
+        return inputs
+
+
+# pylint: disable=W0221
+class PaddedCenterCropCustom(tf.keras.layers.Layer):
+    """Custom keras layer to center crop the input image, same as FedMLB paper."""
+
+    def __init__(self, height: int = 64, width: int = 64, **kwargs):
+        super().__init__(**kwargs)
+        self.height = height
+        self.width = width
+
+    def call(self, inputs: tf.Tensor):
+        """Call the layer on new inputs and returns the outputs as tensors."""
+        input_tensor = tf.image.resize_with_crop_or_pad(
+            image=inputs, target_height=self.height, target_width=self.width
+        )
+
+        input_shape = tf.shape(inputs)
+        h_diff = input_shape[0] - self.height
+        w_diff = input_shape[1] - self.width
+
+        h_start = tf.cast(h_diff / 2, tf.int32)
+        w_start = tf.cast(w_diff / 2, tf.int32)
+        return tf.image.crop_to_bounding_box(
+            input_tensor, h_start, w_start, self.height, self.width
+        )
+
+
+def load_client_datasets_from_files(  # pylint: disable=too-many-arguments
+    # pylint: disable=too-many-locals
+    dataset: str,
+    sampled_client: int,
+    batch_size: int,
+    total_clients: int = 100,
+    alpha: float = 0.3,
+    split: str = "train",
+    seed: Union[int, None] = None,
+):
+    """Load the partition of the dataset for the sampled client.
+
+    Sampled client represented by its client_id. Examples are preprocessed via
+    normalization layer. Returns a batched dataset.
+    """
+
+    def element_fn_norm_cifar100(image, label):
+        """Normalize cifar100 images."""
+        norm_layer = tf.keras.layers.Normalization(
+            mean=[0.5071, 0.4865, 0.4409],
+            variance=[np.square(0.2673), np.square(0.2564), np.square(0.2762)],
+        )
+        return norm_layer(tf.cast(image, tf.float32) / 255.0), label
+
+    def element_fn_norm_tiny_imagenet(image, label):
+        """Normalize tiny-imagenet images."""
+        norm_layer = tf.keras.layers.Normalization(
+            mean=[0.4802, 0.4481, 0.3975],
+            variance=[np.square(0.2770), np.square(0.2691), np.square(0.2821)],
+        )
+        return norm_layer(tf.cast(image, tf.float32) / 255.0), tf.expand_dims(
+            label, axis=-1
+        )
+
+    # transform images
+    rotate = tf.keras.layers.RandomRotation(0.028, seed=seed)
+    flip = tf.keras.layers.RandomFlip(mode="horizontal", seed=seed)
+
+    if dataset in ["cifar100"]:
+        crop = PaddedRandomCropCustom(seed=seed)
+    else:  # tiny-imagenet
+        crop = PaddedRandomCropCustom(seed=seed, height=64, width=64)
+
+    rotate_flip_crop = tf.keras.Sequential(
+        [
+            rotate,
+            crop,
+            flip,
+        ]
+    )
+
+    center_crop = tf.keras.layers.CenterCrop(64, 64)
+
+    def center_crop_data(image, label):
+        """Crop images."""
+        return center_crop(image), label
+
+    def transform_data(image, label):
+        """Transform images."""
+        return rotate_flip_crop(image), label
+
+    path = os.path.join(
+        dataset + "_mlb_dirichlet_train",
+        str(total_clients),
+        str(round(alpha, 2)),
+        split,
+    )
+
+    loaded_ds = tf.data.Dataset.load(
+        path=os.path.join(path, str(sampled_client)),
+        element_spec=None,
+        compression=None,
+        reader_func=None,
+    )
+
+    if dataset in ["cifar100"]:
+        if split == "test":
+            return loaded_ds.map(element_fn_norm_cifar100).batch(
+                batch_size, drop_remainder=False
+            )
+        loaded_ds = (
+            loaded_ds.shuffle(buffer_size=1024, seed=seed)
+            .map(element_fn_norm_cifar100)
+            .map(transform_data)
+            .batch(batch_size, drop_remainder=False)
+        )
+        loaded_ds = loaded_ds.prefetch(tf.data.AUTOTUNE)
+        return loaded_ds
+    # dataset in ["tiny_imagenet"]
+    if split == "test":
+        return (
+            loaded_ds.map(element_fn_norm_tiny_imagenet)
+            .map(center_crop_data)
+            .batch(batch_size, drop_remainder=False)
+        )
+    loaded_ds = (
+        loaded_ds.shuffle(buffer_size=1024, seed=seed)
+        .map(element_fn_norm_tiny_imagenet)
+        .map(transform_data)
+        .batch(batch_size, drop_remainder=False)
+    )
+    loaded_ds = loaded_ds.prefetch(tf.data.AUTOTUNE)
+    return loaded_ds
diff --git a/baselines/fedmlb/fedmlb/dataset_preparation.py b/baselines/fedmlb/fedmlb/dataset_preparation.py
new file mode 100644
index 000000000000..f0a6095de941
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/dataset_preparation.py
@@ -0,0 +1,269 @@
+"""Handle the dataset partitioning of the datasets being used in simulations.
+
+Datasets: CIFAR-100 and TinyImagenet.
+"""
+
+import ast
+import os
+import shutil
+import sys
+from collections import defaultdict
+
+import hydra
+import numpy as np
+import tensorflow as tf
+from omegaconf import DictConfig, OmegaConf
+from PIL import Image
+
+np.set_printoptions(threshold=sys.maxsize)
+
+IMAGENET_BASE_PATH = "./tiny-imagenet-200/"
+
+
+def read_fedmlb_distribution(
+    dataset: str, total_clients: int, alpha: float, data_folder: str = "client_data"
+):
+    """Read the per-client distribution of labels from file.
+
+    Parameters
+    ----------
+    dataset : str
+        Dataset as string.
+    total_clients : int
+        Total clients of the simulation.
+    alpha: float
+        Concentration parameter of Dirichlet distribution for label skew.
+    data_folder: str
+        Name of the folder that contains the files to be read.
+    """
+    if dataset in ["tiny-imagenet"]:
+        dataset = "Tiny-ImageNet"
+
+    file_path = os.path.join(
+        "fedmlb",
+        data_folder,
+        dataset,
+        "balanced",
+        "dirichlet" + str(round(alpha, 1)) + "_clients" + str(total_clients) + ".txt",
+    )
+
+    # reading the data from the file
+    with open(file_path) as distribution_file:
+        data = distribution_file.read()
+
+    # reconstructing the data as a dictionary
+    data_mlb = ast.literal_eval(data)
+
+    return data_mlb
+
+
+class TinyImageNetPaths:
+    """Reader for tiny-imagenet dataset on disk.
+
+    Adapted from:
+    https://github.com/jinkyu032/FedMLB/blob/main/datasets/tiny_imagenet.py
+    """
+
+    def __init__(self):
+        root_dir = IMAGENET_BASE_PATH
+        train_path = os.path.join(root_dir, "train")
+        val_path = os.path.join(root_dir, "val")
+        test_path = os.path.join(root_dir, "test")
+
+        wnids_path = os.path.join(root_dir, "wnids.txt")
+        words_path = os.path.join(root_dir, "words.txt")
+
+        self._make_paths(train_path, val_path, test_path, wnids_path, words_path)
+
+    def _make_paths(  # pylint: disable=too-many-arguments, disable=too-many-locals
+        self, train_path, val_path, test_path, wnids_path, words_path
+    ):
+        self.ids = []
+        with open(wnids_path, "r") as idf:
+            for nid in idf:
+                nid = nid.strip()
+                self.ids.append(nid)
+        self.nid_to_words = defaultdict(list)
+        with open(words_path, "r") as words_file:
+            for line in words_file:
+                nid, labels = line.split("\t")
+                labels = [x.strip() for x in labels.split(",")]
+                self.nid_to_words[nid].extend(labels)
+
+        self.paths = {
+            "train": [],  # [img_path, id, nid, box]
+            "val": [],  # [img_path, id, nid, box]
+            "test": [],  # img_path
+        }
+
+        # Get the test paths
+        self.paths["test"] = [os.path.join(test_path, x) for x in os.listdir(test_path)]
+        # Get the validation paths and labels
+        with open(os.path.join(val_path, "val_annotations.txt")) as valf:
+            for line in valf:
+                file_name, nid, x_0, y_0, x_1, y_1 = line.split()
+                file_name = os.path.join(val_path, "images", file_name)
+                bbox = int(x_0), int(y_0), int(x_1), int(y_1)
+                label_id = self.ids.index(nid)
+                self.paths["val"].append((file_name, label_id, nid, bbox))
+
+        # Get the training paths
+        train_nids = os.listdir(train_path)
+        for nid in train_nids:
+            anno_path = os.path.join(train_path, nid, nid + "_boxes.txt")
+            imgs_path = os.path.join(train_path, nid, "images")
+            label_id = self.ids.index(nid)
+            with open(anno_path, "r") as anno_file:
+                for line in anno_file:
+                    fname, x_0, y_0, x_1, y_1 = line.split()
+                    fname = os.path.join(imgs_path, fname)
+                    bbox = int(x_0), int(y_0), int(x_1), int(y_1)
+                    self.paths["train"].append((fname, label_id, nid, bbox))
+
+
+def pil_loader(path: str):
+    """Convert to an RGB image."""
+    with open(path, "rb") as image_file:
+        with Image.open(image_file) as img:
+            return img.convert("RGB")
+
+
+def load_test_dataset_tiny_imagenet():
+    """Load test dataset for Tiny-imagenet."""
+    path_obj = TinyImageNetPaths()
+    samples = path_obj.paths["val"]
+    data = np.array([i[0] for i in samples])
+
+    targets = np.array([i[1] for i in samples])
+    labels = []
+    images = []
+    for current_image in data:
+        img = pil_loader(current_image)
+        img_np = np.asarray(img)
+        images.append(img_np)
+
+    for target in targets:
+        labels.append(target)
+
+    x_test = np.stack(images, axis=0)
+    y_test = np.stack(labels, axis=0)
+    test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))
+    return test_ds
+
+
+@hydra.main(config_path="conf", config_name="base", version_base=None)
+def download_and_preprocess(cfg: DictConfig) -> None:  # pylint: disable=too-many-locals
+    # pylint: disable=too-many-statements
+    """Do everything needed to get the dataset.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    print(OmegaConf.to_yaml(cfg))
+
+    dataset = cfg.dataset_config.dataset
+    alpha = cfg.dataset_config.alpha_dirichlet
+    total_clients = cfg.total_clients
+
+    folder = dataset + "_mlb_dirichlet_train"
+    if dataset in ["cifar100"]:
+        num_classes = 100
+    else:
+        num_classes = 200
+
+    # if the folder exist it is deleted and the ds partitions are re-created
+    # if the folder does not exist, firstly the folder is created
+    # and then the ds partitions are generated
+    folder_path = os.path.join(folder, str(total_clients), str(round(alpha, 2)))
+    exist = os.path.exists(folder_path)
+    if not exist:
+        os.makedirs(folder_path)
+    else:
+        shutil.rmtree(folder_path, ignore_errors=True)
+
+    if dataset in ["tiny-imagenet"]:
+        path_obj = TinyImageNetPaths()
+
+        samples = path_obj.paths["train"]
+        data = np.array([i[0] for i in samples])
+        targets = np.array([i[1] for i in samples])
+
+        labels = []
+        images = []
+        for current_image in data:
+            img = pil_loader(current_image)
+            img_np = np.asarray(img)
+            images.append(img_np)
+
+        for target in targets:
+            labels.append(target)
+
+        x_train = np.stack(images, axis=0)
+        y_train = np.stack(labels, axis=0)
+
+    elif dataset in ["cifar100"]:
+        (x_train, y_train), (_, _) = tf.keras.datasets.cifar100.load_data()
+
+    # read the distribution of per-label examples for each client
+    # from txt file
+    data_mlb = read_fedmlb_distribution(
+        dataset, total_clients=total_clients, alpha=alpha
+    )
+
+    for client in data_mlb:
+        list_extracted_all_labels = data_mlb[client]
+        numpy_dataset_y = y_train[list_extracted_all_labels]
+        numpy_dataset_x = x_train[list_extracted_all_labels]
+
+        tf_dataset = tf.data.Dataset.from_tensor_slices(
+            (numpy_dataset_x, numpy_dataset_y)
+        )
+        tf_dataset = tf_dataset.shuffle(buffer_size=4096)
+
+        tf.data.Dataset.save(
+            tf_dataset, path=os.path.join(folder_path, "train", str(client))
+        )
+
+    path = os.path.join(os.path.join(folder_path, "train"))
+
+    list_of_narrays = []
+    for sampled_client in range(0, total_clients):
+        loaded_ds = tf.data.Dataset.load(
+            path=os.path.join(path, str(sampled_client)),
+            element_spec=None,
+            compression=None,
+            reader_func=None,
+        )
+
+        print("[Client " + str(sampled_client) + "]")
+        print("Cardinality: ", tf.data.experimental.cardinality(loaded_ds).numpy())
+
+        def count_class(counts, batch, num_classes=num_classes):
+            _, labels = batch
+            for i in range(num_classes):
+                accumulator = tf.cast(labels == i, tf.int32)
+                counts[i] += tf.reduce_sum(accumulator)
+            return counts
+
+        initial_state = {i: 0 for i in range(num_classes)}
+        counts = loaded_ds.reduce(initial_state=initial_state, reduce_func=count_class)
+
+        # print([(k, v.numpy()) for k, v in counts.items()])
+        new_dict = {k: v.numpy() for k, v in counts.items()}
+        # print(new_dict)
+        res = np.array(list(new_dict.values()))
+        # print(res)
+        list_of_narrays.append(res)
+
+    distribution = np.stack(list_of_narrays)
+    print(distribution)
+    # saving the distribution of per-label examples in a numpy file
+    # this can be useful also to draw charts about the label distrib.
+    path = os.path.join(folder_path, "distribution_train.npy")
+    np.save(path, distribution)
+
+
+if __name__ == "__main__":
+    download_and_preprocess()
diff --git a/baselines/fedmlb/fedmlb/fedavg_kd_model.py b/baselines/fedmlb/fedmlb/fedavg_kd_model.py
new file mode 100644
index 000000000000..e80b80eb60a1
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/fedavg_kd_model.py
@@ -0,0 +1,80 @@
+"""Implement FedAvg+KD as tf.keras.Model."""
+
+import tensorflow as tf
+
+
+# pylint: disable=W0223
+class FedAvgKDModel(tf.keras.Model):
+    """FedAvg+KD implementation from the paper https://arxiv.org/abs/2207.06936.
+
+    Based on the original implementation at https://github.com/jinkyu032/FedMLB.
+    In practice, this is applying regular knowledge distillation (KD)
+    -- [Hinton et al.] https://arxiv.org/abs/1503.02531 --
+    at client-side, with the global model of that round working as teacher
+    on local data, providing regularization for the student model
+    (i.e., client model).
+    """
+
+    def __init__(
+        self,
+        model: tf.keras.Model,
+        h_model: tf.keras.Model,
+        kd_loss: tf.keras.losses.Loss,
+        gamma: float = 0.2,
+    ):
+        super().__init__()
+        self.local_model = model
+        self.global_model = h_model
+        self.kd_loss = kd_loss
+        self.gamma = gamma
+
+    def train_step(self, data):
+        """Implement logic for one training step.
+
+        This method can be overridden to support custom training logic.
+        """
+        # Unpack the data. Its structure depends on your model and
+        # on what you pass to `fit()`.
+        x, y = data
+
+        global_output = self.global_model(x, training=True)
+
+        with tf.GradientTape() as tape:
+            local_output = self.local_model(x, training=True)  # Forward pass
+
+            # Compute the loss value
+            # (the loss function is configured in `compile()`)
+            ce_loss = self.compiled_loss(
+                y, local_output, regularization_losses=self.local_model.losses
+            )
+            kd_loss = self.kd_loss(
+                tf.nn.softmax(global_output, axis=1),
+                tf.nn.softmax(local_output, axis=1),
+            )
+            fedkd_loss = (1 - self.gamma) * ce_loss + self.gamma * kd_loss
+
+        # Compute gradients
+        trainable_vars = self.local_model.trainable_variables
+        gradients = tape.gradient(fedkd_loss, trainable_vars)
+        # Update weights
+        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
+        # Update metrics (includes the metric that tracks the loss)
+        self.compiled_metrics.update_state(y, local_output)
+
+        return {m.name: m.result() for m in self.metrics}
+
+    def test_step(self, data):
+        """Implement logic for one evaluation step.
+
+        This method can be overridden to support custom evaluation logic.
+        """
+        x, y = data
+        y_pred = self.local_model(x, training=False)  # Forward pass
+        self.compiled_loss(y, y_pred, regularization_losses=self.local_model.losses)
+        self.compiled_metrics.update_state(y, y_pred)
+        # self.compiled_metrics
+        return {m.name: m.result() for m in self.metrics}
+
+    def get_weights(self):
+        """Return the weights of the local model."""
+        return self.local_model.get_weights()
diff --git a/baselines/fedmlb/fedmlb/fedmlb_model.py b/baselines/fedmlb/fedmlb/fedmlb_model.py
new file mode 100644
index 000000000000..cd6c6e240b21
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/fedmlb_model.py
@@ -0,0 +1,100 @@
+"""Implement FedMLB as tf.keras.Model."""
+
+import tensorflow as tf
+
+
+# pylint: disable=W0223
+class FedMLBModel(tf.keras.Model):
+    """FedMLB implementation from the paper https://arxiv.org/abs/2207.06936.
+
+    Based on the original implementation at https://github.com/jinkyu032/FedMLB.
+    """
+
+    def __init__(
+        self,
+        local_model_mlb: tf.keras.Model,
+        global_model_mlb: tf.keras.Model,
+        kd_loss: tf.keras.losses.Loss,
+        lambda_1: float,
+        lambda_2: float,
+    ):  # pylint: disable=too-many-arguments
+        super().__init__()
+        # both local_model mlb_model are instance of custom mlb model
+        self.local_model = local_model_mlb
+        self.global_model = global_model_mlb
+        self.kd_loss = kd_loss
+        self.lambda_1 = lambda_1
+        self.lambda_2 = lambda_2
+        self.cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(
+            from_logits=True, reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE
+        )
+
+    def train_step(self, data):  # pylint: disable=too-many-locals
+        """Implement logic for one training step.
+
+        This method can be overridden to support custom training logic.
+        """
+        # Unpack the data. Its structure depends on your model and
+        # on what you pass to `fit()`.
+        x, y = data
+
+        with tf.GradientTape() as tape:
+            out_of_local = self.local_model(x, return_feature=True)
+            local_features = out_of_local[:-1]
+            logits = out_of_local[-1]
+
+            ce_branch = []
+            kl_branch = []
+            num_branch = len(local_features)
+
+            ce_loss = self.compiled_loss(
+                y, logits, regularization_losses=self.local_model.losses
+            )
+
+            # Compute loss from hybrid branches
+            for branch in range(num_branch):
+                this_logits = self.global_model(
+                    local_features[branch], level=branch + 1
+                )
+                this_ce = self.cross_entropy(y, this_logits)
+                this_kl = self.kd_loss(
+                    tf.nn.softmax(this_logits), tf.nn.softmax(logits)
+                )
+                ce_branch.append(this_ce)
+                kl_branch.append(this_kl)
+
+            ce_hybrid_loss = tf.reduce_mean(tf.stack(ce_branch))
+            kd_loss = tf.reduce_mean(tf.stack(kl_branch))
+            fedmlb_loss = (
+                ce_loss + self.lambda_1 * ce_hybrid_loss + self.lambda_2 * kd_loss
+            )
+
+        trainable_vars = self.local_model.trainable_variables
+        gradients = tape.gradient(fedmlb_loss, trainable_vars)
+        # Update weights
+        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
+        # Update metrics (includes the metric that tracks the loss)
+        self.compiled_metrics.update_state(y, logits)
+
+        return {m.name: m.result() for m in self.metrics}
+
+    def test_step(self, data):
+        """Implement logic for one evaluation step.
+
+        This method can be overridden to support custom evaluation logic.
+        """
+        x, y = data
+
+        y_pred = self.local_model(x)  # Forward pass
+        self.compiled_loss(y, y_pred, regularization_losses=self.local_model.losses)
+        self.compiled_metrics.update_state(y, y_pred)
+        # self.compiled_metrics
+        return {m.name: m.result() for m in self.metrics}
+
+    def get_weights(self):
+        """Return the weights of the local model."""
+        return self.local_model.get_weights()
+
+    def get_global_weights(self):
+        """Return the weights of the global model."""
+        return self.global_model.get_weights()
diff --git a/baselines/fedmlb/fedmlb/main.py b/baselines/fedmlb/fedmlb/main.py
new file mode 100644
index 000000000000..626a9f7f6e17
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/main.py
@@ -0,0 +1,415 @@
+"""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.
+"""
+
+import os
+import shutil
+from typing import Callable, Dict, Optional, Tuple
+
+# these are the basic packages you'll need here
+# feel free to remove some if aren't needed
+import flwr
+import hydra
+import numpy as np
+import tensorflow as tf
+from flwr.common import NDArrays, Scalar
+from flwr.simulation.ray_transport.utils import enable_tf_gpu_growth
+from hydra.core.hydra_config import HydraConfig
+from hydra.utils import instantiate
+from omegaconf import DictConfig, OmegaConf
+
+import fedmlb.dataset as fedmlb_datasets
+import fedmlb.dataset_preparation as fedmlb_ds_preparation
+import fedmlb.models as fedmlb_models
+from fedmlb.client import TFClient
+from fedmlb.fedavg_kd_model import FedAvgKDModel
+from fedmlb.fedmlb_model import FedMLBModel
+from fedmlb.models import create_resnet18
+from fedmlb.server import MyServer
+from fedmlb.utils import (
+    dic_load,
+    dic_save,
+    get_cpu_memory,
+    get_gpu_memory,
+    save_results_as_pickle,
+)
+
+# Make TensorFlow logs less verbose
+
+
+# os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+enable_tf_gpu_growth()
+
+TEST_BATCH_SIZE = 256
+
+
+@hydra.main(config_path="conf", config_name="base", version_base=None)
+def main(cfg: DictConfig) -> None:  # pylint: disable=too-many-locals
+    # pylint: disable=too-many-statements
+    """Run the baseline.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    print("[Start Simulation]")
+    # Print parsed config
+    print(OmegaConf.to_yaml(cfg))
+
+    def element_norm_fn_cifar100(image, label):
+        """Normalize input images (CIFAR100)."""
+        norm_layer = tf.keras.layers.Normalization(
+            mean=[0.5071, 0.4865, 0.4409],
+            variance=[np.square(0.2673), np.square(0.2564), np.square(0.2762)],
+        )
+        return norm_layer(tf.cast(image, tf.float32) / 255.0), label
+
+    def element_fn_norm_tiny_imagenet(image, label):
+        norm_layer = tf.keras.layers.Normalization(
+            mean=[0.4802, 0.4481, 0.3975],
+            variance=[np.square(0.2770), np.square(0.2691), np.square(0.2821)],
+        )
+        return norm_layer(tf.cast(image, tf.float32) / 255.0), tf.expand_dims(
+            label, axis=-1
+        )
+
+    def get_evaluate_fn(
+        model: tf.keras.Model, save_path: str, dataset: str, starting_round: int
+    ) -> Callable[
+        [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]
+    ]:
+        """Return an evaluation function for server-side evaluation."""
+        if dataset in ["cifar100"]:
+            (_, _), (x_test, y_test) = tf.keras.datasets.cifar100.load_data()
+            test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))
+            test_ds = test_ds.map(element_norm_fn_cifar100).batch(TEST_BATCH_SIZE)
+        else:  # tiny-imagenet
+            # center_crop = tf.keras.layers.CenterCrop(64, 64)
+            center_crop = fedmlb_datasets.PaddedCenterCropCustom(64, 64)
+
+            def center_crop_data(image, label):
+                return center_crop(image), label
+
+            test_ds = fedmlb_ds_preparation.load_test_dataset_tiny_imagenet()
+            test_ds = (
+                test_ds.map(element_fn_norm_tiny_imagenet)
+                .map(center_crop_data)
+                .batch(TEST_BATCH_SIZE)
+            )
+
+        # creating a tensorboard writer to log results
+        # then results can be monitored in real-time with tensorboard
+        # running the command:
+        # tensorboard --logdir [HERE_THE_PATH_OF_TF_BOARD_LOGS]
+        global_summary_writer = tf.summary.create_file_writer(save_path)
+
+        # The `evaluate` function will be called after every round
+        def evaluate(
+            server_round: int,
+            parameters: NDArrays,
+            config: Dict[str, Scalar],  # pylint: disable=unused-argument
+        ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+            model.set_weights(parameters)  # Update model with the latest parameters
+            loss, accuracy = model.evaluate(
+                test_ds,
+            )
+
+            with global_summary_writer.as_default():
+                tf.summary.scalar("loss", loss, step=server_round)
+                tf.summary.scalar("accuracy", accuracy, step=server_round)
+
+                if cfg.logging_memory_usage:
+                    # logging metrics on memory usage
+                    gpu_free_memory = get_gpu_memory()
+                    cpu_free_memory = get_cpu_memory()
+                    tf.summary.scalar(
+                        "cpu_free_mem", cpu_free_memory, step=server_round
+                    )
+                    tf.summary.scalar(
+                        "gpu_free_mem", gpu_free_memory, step=server_round
+                    )
+
+            # saving the checkpoint before the end of simulation
+            if cfg.save_checkpoint and server_round == (
+                cfg.num_rounds + starting_round - 1
+            ):
+                path = os.path.join(
+                    save_path_checkpoints,
+                    "checkpoints_R" + str(server_round),
+                    "server_model",
+                )
+                server_model.save_weights(path)
+
+                path = os.path.join(save_path_checkpoints, "dict_info")
+                dic_save({"checkpoint_round": server_round}, path)
+
+            return loss, {"accuracy": accuracy}
+
+        return evaluate
+
+    def fit_config(server_round: int) -> Dict[str, Scalar]:
+        """Return training configuration dict for each round."""
+        config = {
+            "current_round": server_round,
+            "local_epochs": 5,
+            "exp_decay": cfg.exp_decay,
+            "lr_client_initial": cfg.lr_client,
+        }
+        return config
+
+    ray_init_args = {"include_dashboard": False}
+    # Parse input parameters
+    algorithm = cfg.algorithm
+    random_seed = cfg.random_seed
+    lr_client = cfg.lr_client
+    # exp_decay = cfg.exp_decay
+    clipnorm = cfg.clipnorm
+    l2_weight_decay = cfg.l2_weight_decay
+    lambda_1 = cfg.lambda_1
+    lambda_2 = cfg.lambda_2
+    alpha_dirichlet = cfg.dataset_config.alpha_dirichlet
+    local_updates = cfg.local_updates
+    local_epochs = cfg.local_epochs
+    total_clients = cfg.total_clients
+    dataset = cfg.dataset_config.dataset
+    restart_from_checkpoint = cfg.restart_from_checkpoint
+    batch_size = cfg.batch_size
+
+    # if cfg.batch_size is set to null,
+    # local_batch_size = round(local_examples * local_epochs / local_updates)
+    # if cfg.batch_size is set to a value, it will be used as local_batch_size
+    if batch_size is None:
+        local_batch_size_or_k_defined = "K_" + str(local_updates)
+    else:
+        local_batch_size_or_k_defined = "batch_size_" + str(batch_size)
+
+    if dataset in ["cifar100"]:
+        num_classes = 100
+        input_shape = (None, 32, 32, 3)
+    else:  # tiny-imagenet
+        num_classes = 200
+        input_shape = (None, 64, 64, 3)
+
+    def client_fn(cid: str) -> TFClient:
+        """Instantiate TF Client."""
+        local_examples = fedmlb_datasets.load_selected_client_statistics(
+            int(cid),
+            total_clients=total_clients,
+            alpha=alpha_dirichlet,
+            dataset=dataset,
+        )
+
+        # if cfg.batch_size is set to null,
+        # local_batch_size = round(local_examples * local_epochs / local_updates)
+        # if cfg.batch_size is set to a value, it will be used as local_batch_size
+        if batch_size is None:
+            local_batch_size = round(local_examples * local_epochs / local_updates)
+        else:
+            local_batch_size = batch_size
+
+        training_dataset = fedmlb_datasets.load_client_datasets_from_files(
+            dataset=dataset,
+            sampled_client=int(cid),
+            total_clients=total_clients,
+            batch_size=local_batch_size,
+            alpha=alpha_dirichlet,
+            seed=random_seed,
+        )
+
+        if algorithm in ["FedAvg"]:
+            client_model = fedmlb_models.create_resnet18(
+                num_classes=num_classes,
+                input_shape=input_shape,
+                norm="group",
+                seed=random_seed,
+            )
+            client_model.compile(
+                optimizer=tf.keras.optimizers.SGD(
+                    learning_rate=lr_client,
+                    clipnorm=clipnorm,
+                    weight_decay=l2_weight_decay,
+                ),
+                loss=tf.keras.losses.SparseCategoricalCrossentropy(
+                    from_logits=True,
+                    reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
+                ),
+                metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
+            )
+
+        elif algorithm in ["FedMLB"]:
+            local_model = fedmlb_models.create_resnet18_mlb(
+                num_classes=num_classes,
+                input_shape=input_shape,
+                norm="group",
+                seed=random_seed,
+            )
+            global_model = fedmlb_models.create_resnet18_mlb(
+                num_classes=num_classes,
+                input_shape=input_shape,
+                norm="group",
+                seed=random_seed,
+            )
+            kd_loss = tf.keras.losses.KLDivergence(
+                reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE
+            )
+
+            client_model = FedMLBModel(
+                local_model, global_model, kd_loss, lambda_1, lambda_2
+            )
+            client_model.compile(
+                optimizer=tf.keras.optimizers.SGD(
+                    learning_rate=lr_client,
+                    clipnorm=clipnorm,
+                    weight_decay=l2_weight_decay,
+                ),
+                loss=tf.keras.losses.SparseCategoricalCrossentropy(
+                    from_logits=True,
+                    reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
+                ),
+                metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
+            )
+
+        else:  # algorithm in ["FedAvg+KD"]:
+            local_model = fedmlb_models.create_resnet18(
+                num_classes=num_classes,
+                input_shape=input_shape,
+                norm="group",
+                seed=random_seed,
+            )
+            global_model = fedmlb_models.create_resnet18(
+                num_classes=num_classes,
+                input_shape=input_shape,
+                norm="group",
+                seed=random_seed,
+            )
+            kd_loss = tf.keras.losses.KLDivergence(
+                reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE
+            )
+            client_model = FedAvgKDModel(local_model, global_model, kd_loss, gamma=0.2)
+
+            client_model.compile(
+                optimizer=tf.keras.optimizers.SGD(
+                    learning_rate=lr_client,
+                    clipnorm=clipnorm,
+                    weight_decay=l2_weight_decay,
+                ),
+                loss=tf.keras.losses.SparseCategoricalCrossentropy(
+                    from_logits=True,
+                    reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
+                ),
+                metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
+            )
+
+        client = TFClient(training_dataset, client_model, local_examples, algorithm)
+
+        # Create and return client
+        return client
+
+    server_model = create_resnet18(
+        num_classes=num_classes,
+        input_shape=input_shape,
+        norm="group",
+        seed=cfg.random_seed,
+    )
+    server_model.compile(
+        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
+    )
+
+    save_path_logging = os.path.join(
+        "fedmlb",
+        "tb_logging",
+        dataset,
+        "resnet18",
+        algorithm,
+        local_batch_size_or_k_defined,
+        str(total_clients) + "_clients",
+        "dir_" + str(round(alpha_dirichlet, 1)),
+        "seed_" + str(random_seed),
+    )
+
+    save_path_checkpoints = os.path.join(
+        "fedmlb",
+        "model_checkpoints",
+        dataset,
+        "resnet18",
+        algorithm,
+        local_batch_size_or_k_defined,
+        str(total_clients) + "_clients",
+        "dir_" + str(round(alpha_dirichlet, 1)),
+        "seed_" + str(random_seed),
+    )
+
+    starting_round = 1
+    if restart_from_checkpoint:
+        # if there is a checkpoint and restart_from_checkpoint is True
+        # the training restart from the state saved in the most recent checkpoint
+        # i.e., the one indicated in a dictionary named dict_info
+        path = os.path.join(save_path_checkpoints, "dict_info.pickle")
+        last_checkpoint = dic_load(path)["checkpoint_round"]
+        if last_checkpoint:
+            print(f"Loading saved checkpoint round {last_checkpoint}")
+            path = os.path.join(
+                save_path_checkpoints,
+                "checkpoints_R" + str(last_checkpoint),
+                "server_model",
+            )
+            server_model.load_weights(path)
+            starting_round = last_checkpoint + 1
+    else:
+        # this will delete the checkpoints of previous simulations for that config
+        exist = os.path.exists(save_path_checkpoints)
+        if exist:
+            shutil.rmtree(save_path_checkpoints, ignore_errors=True)
+
+    tf.keras.utils.set_random_seed(cfg.random_seed * starting_round)
+    params = server_model.get_weights()
+
+    strategy = instantiate(
+        cfg.strategy,
+        initial_parameters=flwr.common.ndarrays_to_parameters(params),
+        evaluate_fn=get_evaluate_fn(
+            server_model, save_path_logging, dataset, starting_round
+        ),
+        on_fit_config_fn=fit_config,
+    )
+
+    # my_server = MyServer(cfg.starting_round)
+    my_server = MyServer(strategy=strategy, starting_round=starting_round)
+    # Start Flower simulation
+    history = flwr.simulation.start_simulation(
+        client_fn=client_fn,
+        num_clients=cfg.total_clients,
+        client_resources={
+            "num_cpus": cfg.client_resources.num_cpus,
+            "num_gpus": cfg.client_resources.num_gpus,
+        },
+        server=my_server,
+        config=flwr.server.ServerConfig(num_rounds=cfg.num_rounds),
+        ray_init_args=ray_init_args,
+        actor_kwargs={
+            "on_actor_init_fn": enable_tf_gpu_growth
+            # Enable GPU growth upon actor init
+            # does nothing if `num_gpus` in client_resources is 0.0
+        },
+    )
+
+    # Experiment completed. Now we save the results and
+    # generate plots using the `history`
+    print("................")
+    print(history)
+
+    # Hydra automatically creates an output directory
+    # Let's retrieve it and save some results there
+    save_path = HydraConfig.get().runtime.output_dir
+
+    # save results as a Python pickle using a file_path
+    # the directory created by Hydra for each run
+    save_results_as_pickle(history, file_path=save_path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/fedmlb/fedmlb/models.py b/baselines/fedmlb/fedmlb/models.py
new file mode 100644
index 000000000000..d079ac871680
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/models.py
@@ -0,0 +1,434 @@
+"""Define custom models being used."""
+
+from typing import Optional, Tuple, Union
+
+import tensorflow as tf
+
+BATCH_NORM_DECAY = 0.997
+BATCH_NORM_EPSILON = 1e-5
+LAYER_NORM_EPSILON = 1e-5
+GROUP_NORM_EPSILON = 1e-5
+
+
+def get_norm_layer(norm: str, channel_axis: int) -> tf.keras.layers.Layer:
+    """Return the requested norm layer."""
+    if norm == "batch":
+        return tf.keras.layers.BatchNormalization(
+            axis=channel_axis, momentum=BATCH_NORM_DECAY
+        )
+
+    if norm == "layer":
+        return tf.keras.layers.LayerNormalization(
+            axis=channel_axis, epsilon=LAYER_NORM_EPSILON
+        )
+
+    return tf.keras.layers.GroupNormalization(groups=2, epsilon=GROUP_NORM_EPSILON)
+
+
+# pylint: disable=W0221
+class ResBlock(tf.keras.Model):
+    """Implement a ResBlock."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        filters: int,
+        downsample: bool,
+        norm: str = "group",
+        l2_weight_decay: float = 1e-3,
+        stride: int = 1,
+        seed: Optional[Union[int, None]] = None,
+    ):
+        super().__init__()
+
+        if tf.keras.backend.image_data_format() == "channels_last":
+            channel_axis = 3
+        else:
+            channel_axis = 1
+
+        self.conv1 = tf.keras.layers.Conv2D(
+            filters,
+            kernel_size=(3, 3),
+            strides=stride,
+            padding="same",
+            use_bias=False,
+            kernel_initializer=tf.keras.initializers.HeNormal(seed=seed),
+            kernel_regularizer=tf.keras.regularizers.l2(l2_weight_decay),
+        )
+
+        if downsample:
+            self.shortcut = tf.keras.Sequential(
+                [
+                    tf.keras.layers.Conv2D(
+                        filters,
+                        kernel_size=(1, 1),
+                        strides=stride,
+                        padding="valid",
+                        use_bias=False,
+                        kernel_initializer=tf.keras.initializers.HeNormal(seed=seed),
+                        kernel_regularizer=tf.keras.regularizers.l2(l2_weight_decay),
+                    ),
+                    get_norm_layer(norm, channel_axis=channel_axis),
+                ]
+            )
+        else:
+            self.shortcut = tf.keras.Sequential()
+
+        self.gn1 = get_norm_layer(norm, channel_axis=channel_axis)
+
+        self.conv2 = tf.keras.layers.Conv2D(
+            filters,
+            kernel_size=(3, 3),
+            strides=(1, 1),
+            padding="same",
+            use_bias=False,
+            kernel_initializer=tf.keras.initializers.HeNormal(seed=seed),
+            kernel_regularizer=tf.keras.regularizers.l2(l2_weight_decay),
+        )
+        self.gn2 = get_norm_layer(norm, channel_axis=channel_axis)
+
+    def call(
+        self,
+        x: tf.Tensor,
+    ):
+        """Call the model on new inputs and returns the outputs as tensors."""
+        shortcut = self.shortcut(x)
+
+        x = self.conv1(x)
+        x = self.gn1(x)
+        x = tf.keras.layers.ReLU()(x)
+
+        x = self.conv2(x)
+        x = self.gn2(x)
+
+        x = x + shortcut
+        return tf.keras.layers.ReLU()(x)
+
+
+# pylint: disable=W0221
+class ResNet18(tf.keras.Model):
+    """Implement a ResNet18 architecture as in FedMLB paper."""
+
+    def __init__(
+        self,
+        outputs: int = 10,
+        l2_weight_decay: float = 1e-3,
+        norm: str = "",
+        seed: Optional[Union[int, None]] = None,
+    ):
+        super().__init__()
+        if seed is not None:
+            tf.random.set_seed(seed)
+        if tf.keras.backend.image_data_format() == "channels_last":
+            channel_axis = 3
+        else:
+            channel_axis = 1
+
+        self.layer0 = tf.keras.Sequential(
+            [
+                tf.keras.layers.Conv2D(
+                    64,
+                    kernel_size=(3, 3),
+                    strides=(1, 1),
+                    padding="same",
+                    use_bias=False,
+                    kernel_initializer=tf.keras.initializers.HeNormal(seed=seed),
+                    kernel_regularizer=tf.keras.regularizers.l2(l2_weight_decay),
+                ),
+                get_norm_layer(norm, channel_axis=channel_axis),
+                tf.keras.layers.ReLU(),
+            ],
+            name="layer0",
+        )
+
+        self.layer1 = tf.keras.Sequential(
+            [
+                ResBlock(
+                    64,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+                ResBlock(
+                    64,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+            ],
+            name="layer1",
+        )
+
+        self.layer2 = tf.keras.Sequential(
+            [
+                ResBlock(
+                    128,
+                    downsample=True,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=2,
+                    norm=norm,
+                ),
+                ResBlock(
+                    128,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+            ],
+            name="layer2",
+        )
+
+        self.layer3 = tf.keras.Sequential(
+            [
+                ResBlock(
+                    256,
+                    downsample=True,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=2,
+                    norm=norm,
+                ),
+                ResBlock(
+                    256,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+            ],
+            name="layer3",
+        )
+
+        self.layer4 = tf.keras.Sequential(
+            [
+                ResBlock(
+                    512,
+                    downsample=True,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=2,
+                    norm=norm,
+                ),
+                ResBlock(
+                    512,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+            ],
+            name="layer4",
+        )
+
+        self.gap = tf.keras.Sequential(
+            [tf.keras.layers.GlobalAveragePooling2D()], name="gn_relu_gap"
+        )
+        self.fully_connected = tf.keras.layers.Dense(
+            outputs,
+            kernel_initializer=tf.keras.initializers.RandomNormal(
+                stddev=0.01, seed=seed
+            ),
+            kernel_regularizer=tf.keras.regularizers.l2(l2_weight_decay),
+            bias_regularizer=tf.keras.regularizers.l2(l2_weight_decay),
+        )
+
+    def call(self, x: tf.Tensor):
+        """Call the model on new inputs and returns the outputs as tensors."""
+        x = self.layer0(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.gap(x)
+        x = self.fully_connected(x)
+
+        return x
+
+
+# pylint: disable=W0221
+class ResNet18MLB(tf.keras.Model):
+    """Implement a custom ResNet18 architecture as in FedMLB paper."""
+
+    def __init__(
+        self,
+        outputs: int = 10,
+        l2_weight_decay: float = 1e-3,
+        norm: str = "",
+        seed: Optional[Union[int, None]] = None,
+    ):
+        super().__init__()
+        if seed is not None:
+            tf.random.set_seed(seed)
+        if tf.keras.backend.image_data_format() == "channels_last":
+            channel_axis = 3
+        else:
+            channel_axis = 1
+
+        self.layer0 = tf.keras.Sequential(
+            [
+                tf.keras.layers.Conv2D(
+                    64,
+                    kernel_size=(3, 3),
+                    strides=(1, 1),
+                    padding="same",
+                    use_bias=False,
+                    kernel_initializer=tf.keras.initializers.HeNormal(seed=seed),
+                    kernel_regularizer=tf.keras.regularizers.l2(l2_weight_decay),
+                ),
+                get_norm_layer(norm, channel_axis=channel_axis),
+                tf.keras.layers.ReLU(),
+            ],
+            name="layer0",
+        )
+
+        self.layer1 = tf.keras.Sequential(
+            [
+                ResBlock(
+                    64,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+                ResBlock(
+                    64,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+            ],
+            name="layer1",
+        )
+
+        self.layer2 = tf.keras.Sequential(
+            [
+                ResBlock(
+                    128,
+                    downsample=True,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=2,
+                    norm=norm,
+                ),
+                ResBlock(
+                    128,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+            ],
+            name="layer2",
+        )
+
+        self.layer3 = tf.keras.Sequential(
+            [
+                ResBlock(
+                    256,
+                    downsample=True,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=2,
+                    norm=norm,
+                ),
+                ResBlock(
+                    256,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+            ],
+            name="layer3",
+        )
+
+        self.layer4 = tf.keras.Sequential(
+            [
+                ResBlock(
+                    512,
+                    downsample=True,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=2,
+                    norm=norm,
+                ),
+                ResBlock(
+                    512,
+                    downsample=False,
+                    l2_weight_decay=l2_weight_decay,
+                    stride=1,
+                    norm=norm,
+                ),
+            ],
+            name="layer4",
+        )
+
+        self.gap = tf.keras.Sequential(
+            [tf.keras.layers.GlobalAveragePooling2D()], name="gn_relu_gap"
+        )
+        self.fully_connected = tf.keras.layers.Dense(
+            outputs,
+            kernel_initializer=tf.keras.initializers.RandomNormal(
+                stddev=0.01, seed=seed
+            ),
+            kernel_regularizer=tf.keras.regularizers.l2(l2_weight_decay),
+            bias_regularizer=tf.keras.regularizers.l2(l2_weight_decay),
+        )
+
+    def call(self, inputs: tf.Tensor, return_feature: bool = False, level: int = 0):
+        """Call the model on new inputs and returns the outputs as tensors."""
+        if level <= 0:
+            out0 = self.layer0(inputs)
+        else:
+            out0 = inputs
+        if level <= 1:
+            out1 = self.layer1(out0)
+        else:
+            out1 = out0
+        if level <= 2:
+            out2 = self.layer2(out1)
+        else:
+            out2 = out1
+        if level <= 3:
+            out3 = self.layer3(out2)
+        else:
+            out3 = out2
+        if level <= 4:
+            out4 = self.layer4(out3)
+            out4 = self.gap(out4)
+        else:
+            out4 = out3
+
+        logit = self.fully_connected(out4)
+
+        if return_feature:
+            return out0, out1, out2, out3, out4, logit
+        return logit
+
+
+def create_resnet18(
+    num_classes: int = 100,
+    input_shape: Tuple = (None, 32, 32, 3),
+    norm: str = "group",
+    l2_weight_decay: float = 0.0,
+    seed: Optional[Union[int, None]] = None,
+) -> tf.keras.Model:
+    """Return a built ResNet model."""
+    resnet18 = ResNet18(
+        outputs=num_classes, l2_weight_decay=l2_weight_decay, seed=seed, norm=norm
+    )
+    resnet18.build(input_shape)
+    return resnet18
+
+
+def create_resnet18_mlb(
+    num_classes: int = 100,
+    input_shape: Tuple = (None, 32, 32, 3),
+    norm: str = "group",
+    l2_weight_decay: float = 0.0,
+    seed: Optional[Union[int, None]] = None,
+) -> tf.keras.Model:
+    """Return a built ResNetMLB model."""
+    resnet18 = ResNet18MLB(
+        outputs=num_classes, l2_weight_decay=l2_weight_decay, seed=seed, norm=norm
+    )
+    resnet18.build(input_shape)
+    return resnet18
diff --git a/baselines/fedmlb/fedmlb/server.py b/baselines/fedmlb/fedmlb/server.py
new file mode 100644
index 000000000000..fcc893d03a03
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/server.py
@@ -0,0 +1,119 @@
+"""Defines a custom Server."""
+
+import timeit
+from logging import INFO
+from typing import Optional
+
+from flwr.common.logger import log
+from flwr.server.client_manager import ClientManager, SimpleClientManager
+from flwr.server.history import History
+from flwr.server.server import Server
+from flwr.server.strategy import Strategy
+
+
+class MyServer(Server):
+    """Custom Flower server.
+
+    This customization of the server has the only scope to allow to start the training
+    from a starting round different from 1. This is useful when you want to stop and
+    restart the training (saving and loading the state obviously).
+    """
+
+    def __init__(
+        self,
+        *,
+        client_manager: Optional[ClientManager] = None,
+        strategy: Optional[Strategy] = None,
+        starting_round: int = 1,
+    ) -> None:
+        if client_manager is None:
+            client_manager = SimpleClientManager()
+        super().__init__(client_manager=client_manager, strategy=strategy)
+        print(self.client_manager)
+        self.starting_round = starting_round
+
+    # overwriting
+    def fit(  # pylint: disable=too-many-locals
+        self, num_rounds: int, timeout: Optional[float]
+    ) -> History:
+        """Run federated averaging for a number of rounds."""
+        history = History()
+
+        # Initialize parameters
+        log(INFO, "Initializing global parameters")
+        self.parameters = self._get_initial_parameters(timeout=timeout)
+        if self.starting_round == 1:
+            log(INFO, "Evaluating initial parameters")
+            res = self.strategy.evaluate(
+                self.starting_round - 1, parameters=self.parameters
+            )
+            if res is not None:
+                log(
+                    INFO,
+                    "initial parameters (loss, other metrics): %s, %s",
+                    res[0],
+                    res[1],
+                )
+                history.add_loss_centralized(
+                    server_round=self.starting_round - 1, loss=res[0]
+                )
+                history.add_metrics_centralized(
+                    server_round=self.starting_round - 1, metrics=res[1]
+                )
+
+        # Run federated learning for num_rounds
+        log(INFO, "FL starting")
+        start_time = timeit.default_timer()
+
+        # changed this
+        # for current_round in range(1, num_rounds + 1):
+        for current_round in range(
+            self.starting_round, self.starting_round + num_rounds
+        ):
+            # Train model and replace previous global model
+            res_fit = self.fit_round(
+                server_round=current_round,
+                timeout=timeout,
+            )
+            if res_fit is not None:
+                parameters_prime, fit_metrics, _ = res_fit  # fit_metrics_aggregated
+                if parameters_prime:
+                    self.parameters = parameters_prime
+                history.add_metrics_distributed_fit(
+                    server_round=current_round, metrics=fit_metrics
+                )
+
+            # Evaluate model using strategy implementation
+            res_cen = self.strategy.evaluate(current_round, parameters=self.parameters)
+            if res_cen is not None:
+                loss_cen, metrics_cen = res_cen
+                log(
+                    INFO,
+                    "fit progress: (%s, %s, %s, %s)",
+                    current_round,
+                    loss_cen,
+                    metrics_cen,
+                    timeit.default_timer() - start_time,
+                )
+                history.add_loss_centralized(server_round=current_round, loss=loss_cen)
+                history.add_metrics_centralized(
+                    server_round=current_round, metrics=metrics_cen
+                )
+
+            # Evaluate model on a sample of available clients
+            res_fed = self.evaluate_round(server_round=current_round, timeout=timeout)
+            if res_fed is not None:
+                loss_fed, evaluate_metrics_fed, _ = res_fed
+                if loss_fed is not None:
+                    history.add_loss_distributed(
+                        server_round=current_round, loss=loss_fed
+                    )
+                    history.add_metrics_distributed(
+                        server_round=current_round, metrics=evaluate_metrics_fed
+                    )
+
+        # Bookkeeping
+        end_time = timeit.default_timer()
+        elapsed = end_time - start_time
+        log(INFO, "FL finished in %s", elapsed)
+        return history
diff --git a/baselines/fedmlb/fedmlb/simulation_manager.sh b/baselines/fedmlb/fedmlb/simulation_manager.sh
new file mode 100644
index 000000000000..3d0b5ff58eeb
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/simulation_manager.sh
@@ -0,0 +1,10 @@
+#!/usr/bin/bash
+rounds_per_run=20
+total_rounds=1000
+iterations=$total_rounds/$rounds_per_run
+
+for (( i=1; i <= iterations; ++i ))
+do
+    echo "$i"
+    python -m fedmlb.main num_rounds=$rounds_per_run
+done
diff --git a/baselines/fedmlb/fedmlb/strategy.py b/baselines/fedmlb/fedmlb/strategy.py
new file mode 100644
index 000000000000..228a437269ea
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/strategy.py
@@ -0,0 +1 @@
+"""Optionally define a custom strategy."""
diff --git a/baselines/fedmlb/fedmlb/utils.py b/baselines/fedmlb/fedmlb/utils.py
new file mode 100644
index 000000000000..1697e39a3978
--- /dev/null
+++ b/baselines/fedmlb/fedmlb/utils.py
@@ -0,0 +1,107 @@
+"""Contain utility functions."""
+
+import os
+import pickle
+import subprocess as sp
+from os import PathLike
+from secrets import token_hex
+from typing import Dict, Union
+
+import psutil
+from flwr.server.history import History
+
+
+def dic_save(dictionary: Dict[str, int], filename: str):
+    """Save a dictionary to file.
+
+    Parameters
+    ----------
+    dictionary :
+        Dictionary to be saves.
+    filename : str
+        Path to save the dictionary to.
+    """
+    with open(filename + ".pickle", "wb") as dictionary_file:
+        pickle.dump(dictionary, dictionary_file, pickle.HIGHEST_PROTOCOL)
+
+
+def dic_load(filename: str) -> Dict[str, int]:
+    """Load a dictionary from file.
+
+    Parameters
+    ----------
+    filename : str
+        Path to load the dictionary from.
+    """
+    try:
+        with open(filename, "rb") as dictionary_file:
+            return pickle.load(dictionary_file)
+    except IOError:
+        return {"checkpoint_round": 0}
+
+
+def save_results_as_pickle(
+    history: History,
+    file_path: Union[str, PathLike],
+) -> None:
+    """Save results from simulation to pickle.
+
+    Parameters
+    ----------
+    history: History
+        History returned by start_simulation.
+    file_path: Union[str, Path]
+        Path to file to create and store history.
+    """
+
+    def _add_random_suffix(file_name: str):
+        """Add a randomly generated suffix to the file name."""
+        suffix = token_hex(4)
+        print(f"New results to be saved with suffix: {suffix}")
+        return file_name + "_" + suffix + ".pkl"
+
+    filename = "results.pkl"
+    file_path = os.path.join(file_path, filename)
+
+    if os.path.isfile(file_path):
+        filename = _add_random_suffix("results")
+        file_path = os.path.join(file_path, filename)
+
+    print(f"Results will be saved into: {file_path}")
+
+    data = {"history": history}
+
+    # save results to pickle
+    with open(file_path, "wb") as handle:
+        pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+
+def get_gpu_memory() -> float:
+    """Return gpu free memory."""
+    command = "nvidia-smi --query-gpu=memory.free --format=csv"
+    memory_free_info = (
+        sp.check_output(command.split()).decode("ascii").split("\n")[:-1][1:]
+    )
+    memory_free_values = [int(x.split()[0]) for i, x in enumerate(memory_free_info)][0]
+    memory_percent = (memory_free_values / 24564) * 100
+    print(
+        f"[Memory monitoring] Free memory GPU "
+        f"{memory_free_values} MB, {memory_percent} %."
+    )
+    return memory_free_values
+
+
+def get_cpu_memory() -> float:
+    """Return cpu free memory."""
+    # you can convert that object to a dictionary
+    memory_info = psutil.virtual_memory()
+    # you can have the percentage of used RAM
+    memory_percent = 100.0 - memory_info.percent
+    memory_free_values = memory_info.available / (1024 * 1024)  # in MB
+
+    print(
+        f"[Memory monitoring] Free memory CPU "
+        f"{memory_free_values} MB, {memory_percent} %."
+    )
+    # you can calculate percentage of available memory
+    return memory_free_values
diff --git a/baselines/fedmlb/pyproject.toml b/baselines/fedmlb/pyproject.toml
new file mode 100644
index 000000000000..005803e1891e
--- /dev/null
+++ b/baselines/fedmlb/pyproject.toml
@@ -0,0 +1,140 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "fedmlb" # <----- Ensure it matches the name of your baseline directory containing all the source code
+version = "1.0.0"
+description = "FedMLB Flower Baseline"
+license = "Apache-2.0"
+authors = ["Alessio Mora <alessio.mora@unibo.it>"]
+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"  # don't change this
+hydra-core = "1.3.2" # don't change this
+flwr = { extras = ["simulation"], version = "1.5.0" }
+tensorflow = "2.12.0"
+matplotlib = "3.7.1"
+psutil = "5.9.5" # this is just to log some info about memory usage
+
+[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"
+
+[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"

From a6adeabfe64dc10fd065347fc4b641fe83bcbbc3 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 10 Oct 2023 16:16:01 +0200
Subject: [PATCH 52/60] Improve weblate doc naming (#2495)

---
 ... contributor-how-to-contribute-translations.rst} | 13 ++++++-------
 doc/source/index.rst                                |  2 +-
 2 files changed, 7 insertions(+), 8 deletions(-)
 rename doc/source/{contributor-how-to-improve-docs-translation.rst => contributor-how-to-contribute-translations.rst} (94%)

diff --git a/doc/source/contributor-how-to-improve-docs-translation.rst b/doc/source/contributor-how-to-contribute-translations.rst
similarity index 94%
rename from doc/source/contributor-how-to-improve-docs-translation.rst
rename to doc/source/contributor-how-to-contribute-translations.rst
index e8edcee352a2..d97a2cb8c64f 100644
--- a/doc/source/contributor-how-to-improve-docs-translation.rst
+++ b/doc/source/contributor-how-to-contribute-translations.rst
@@ -1,5 +1,5 @@
-Improve docs translations
-=========================
+Contribute translations
+=======================
 
 Since `Flower 1.5
 <https://flower.dev/docs/framework/ref-changelog.html#v1-5-0-2023-08-31>`_ we
@@ -14,8 +14,8 @@ 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.
 
-Contributing to existing languages
-----------------------------------
+Contribute to existing languages
+--------------------------------
 
 .. youtube:: 10_Xfy5BOfQ
    :width: 100%
@@ -63,10 +63,9 @@ file containing the string.
 For more information about translating using Weblate, you can check out this
 `in-depth guide <https://docs.weblate.org/en/latest/user/translating.html>`_.
 
-Adding new languages
---------------------
+Add new languages
+-----------------
 
 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>`_.
-
diff --git a/doc/source/index.rst b/doc/source/index.rst
index 55e81dbaa098..c4a313414d3a 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -159,7 +159,7 @@ The Flower community welcomes contributions. The following docs are intended to
    contributor-how-to-create-new-messages
    contributor-how-to-write-documentation
    contributor-how-to-release-flower
-   contributor-how-to-improve-docs-translation
+   contributor-how-to-contribute-translations
 
 .. toctree::
    :maxdepth: 1

From ce71386739d7a8aafc3c43416a527a8170227096 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Tue, 10 Oct 2023 16:22:11 +0200
Subject: [PATCH 53/60] Fix Flower Datasets API docs (#2422)

---
 .github/workflows/docs.yml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index f8efc09e5120..52944ffecf70 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -27,8 +27,12 @@ jobs:
         uses: ./.github/actions/bootstrap
       - name: Install pandoc
         run: sudo apt install pandoc
-      - name: Install dependencies (mandatory only)
+      - name: Install Flower dependencies (mandatory only)
         run: python -m poetry install --extras "simulation"
+      - name: Install Flower Datasets
+        run: |
+          cd datasets
+          python -m poetry install
       - name: Build docs
         run: ./dev/build-docs.sh
       - name: Deploy docs

From dbab574cfc3eba5ab8b5e12279228c4539a0a917 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ivan=20Agarsk=C3=BD?= <agarskyivan@gmail.com>
Date: Tue, 10 Oct 2023 19:00:46 +0200
Subject: [PATCH 54/60] Add TAMUNA baseline (#2254)

* skeleton code

* client sampling and probability of server communication

* simple file saving and loading

* Create Untitled.ipynb

* upd

* stateful clients + tamuna without compression

* Update compress mask.ipynb

* mask_generator

* Update Mask_generator.ipynb

* compression

* cleanup

* add docstrings and cleanup

* compression pattern

* remove jupyter notebooks

* bugfix

* add power law data splitting + better plotting

* add comparison with fedavg + better plotting

* fix previous client states removal

* add docs images

* write readme for tamuna baseline

* Update README.md

* better formatting

* add plot grid and bump flower version

* Update baselines/tamuna/README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update README.md

* use linux pytorch links in pyproject.toml

* Update README.md

* Update baselines/tamuna/README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/tamuna/pyproject.toml

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* Update baselines/tamuna/pyproject.toml

Co-authored-by: Javier <jafermarq@users.noreply.github.com>

* refactoring

* bugfix

* disable some pylint warning

* use built-in FedAvg for CentralizedFedAvg

* update doc plots

* Update pyproject.toml

* remove client device

---------

Co-authored-by: Malinovsky Grigory <alarcoelectro@gmail.com>
Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/tamuna/.gitignore                   |   4 +
 baselines/tamuna/LICENSE                      | 202 +++++++++++
 baselines/tamuna/README.md                    |  97 ++++++
 .../_static/communication_complexity.png      | Bin 0 -> 153738 bytes
 baselines/tamuna/_static/loss_accuracy.png    | Bin 0 -> 218661 bytes
 baselines/tamuna/pyproject.toml               | 141 ++++++++
 baselines/tamuna/tamuna/__init__.py           |   1 +
 baselines/tamuna/tamuna/client.py             | 236 +++++++++++++
 baselines/tamuna/tamuna/conf/base-cpu.yaml    |  34 ++
 baselines/tamuna/tamuna/conf/base.yaml        |  34 ++
 baselines/tamuna/tamuna/dataset.py            |  34 ++
 .../tamuna/tamuna/dataset_preparation.py      | 200 +++++++++++
 baselines/tamuna/tamuna/main.py               | 211 +++++++++++
 baselines/tamuna/tamuna/models.py             | 328 ++++++++++++++++++
 baselines/tamuna/tamuna/server.py             |  53 +++
 baselines/tamuna/tamuna/strategy.py           | 120 +++++++
 baselines/tamuna/tamuna/utils.py              | 217 ++++++++++++
 17 files changed, 1912 insertions(+)
 create mode 100644 baselines/tamuna/.gitignore
 create mode 100644 baselines/tamuna/LICENSE
 create mode 100644 baselines/tamuna/README.md
 create mode 100644 baselines/tamuna/_static/communication_complexity.png
 create mode 100644 baselines/tamuna/_static/loss_accuracy.png
 create mode 100644 baselines/tamuna/pyproject.toml
 create mode 100644 baselines/tamuna/tamuna/__init__.py
 create mode 100644 baselines/tamuna/tamuna/client.py
 create mode 100644 baselines/tamuna/tamuna/conf/base-cpu.yaml
 create mode 100644 baselines/tamuna/tamuna/conf/base.yaml
 create mode 100644 baselines/tamuna/tamuna/dataset.py
 create mode 100644 baselines/tamuna/tamuna/dataset_preparation.py
 create mode 100644 baselines/tamuna/tamuna/main.py
 create mode 100644 baselines/tamuna/tamuna/models.py
 create mode 100644 baselines/tamuna/tamuna/server.py
 create mode 100644 baselines/tamuna/tamuna/strategy.py
 create mode 100644 baselines/tamuna/tamuna/utils.py

diff --git a/baselines/tamuna/.gitignore b/baselines/tamuna/.gitignore
new file mode 100644
index 000000000000..aba009c52880
--- /dev/null
+++ b/baselines/tamuna/.gitignore
@@ -0,0 +1,4 @@
+outputs/
+dataset/
+client_states/
+model_dim.txt
diff --git a/baselines/tamuna/LICENSE b/baselines/tamuna/LICENSE
new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/tamuna/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
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+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
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+      "Source" form shall mean the preferred form for making modifications,
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+   6. Trademarks. This License does not grant permission to use the trade
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+      whether in tort (including negligence), contract, or otherwise,
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diff --git a/baselines/tamuna/README.md b/baselines/tamuna/README.md
new file mode 100644
index 000000000000..bb4d791e295d
--- /dev/null
+++ b/baselines/tamuna/README.md
@@ -0,0 +1,97 @@
+---
+title: "TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation"
+url: https://arxiv.org/abs/2302.09832
+labels: [local training, communication compression, partial participation, variance reduction]
+dataset: [MNIST]
+---
+
+# Tamuna 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.
+
+****Paper:**** [arxiv.org/abs/2302.09832](https://arxiv.org/abs/2302.098320)
+
+****Authors:**** Laurent Condat, Ivan Agarský, Grigory Malinovsky, Peter Richtárik
+
+****Abstract:**** In federated learning, a large number of users collaborate to learn a global model. They alternate local computations and communication with a distant server. Communication, which can be slow and costly, is the main bottleneck in this setting. In addition to communication-efficiency, a robust algorithm should allow for partial participation, the desirable feature that not all clients need to participate to every round of the training process. To reduce the communication load and therefore accelerate distributed gradient descent, two strategies are popular: 1) communicate less frequently; that is, perform several iterations of local computations between the communication rounds; and 2) communicate compressed information instead of full-dimensional vectors. We propose TAMUNA, the first algorithm for distributed optimization and federated learning, which harnesses these two strategies jointly and allows for partial participation. TAMUNA converges linearly to an exact solution in the strongly convex setting, with a doubly accelerated rate: it provably benefits from the two acceleration mechanisms provided by local training and compression, namely a better dependency on the condition number of the functions and on the model dimension, respectively.
+
+
+## About this baseline
+
+****What’s implemented:**** The code in this directory compares Tamuna with FedAvg. It produces three plots comparing loss, accuracy and communication complexity of the two algorithms. 
+
+****Datasets:**** MNIST
+
+****Hardware Setup:**** By default, the experiments expect at least one gpu, but this can be changed to cpu only by specifying client and server devices. Default setup less than 5 GB of dedicated GPU memory.
+
+****Contributors:**** Ivan Agarský [github.com/Crabzmatic](https://github.com/Crabzmatic), Grigory Malinovsky [github.com/gsmalinovsky](https://github.com/gsmalinovsky)
+
+
+## Experimental Setup
+
+****Task:**** image classification
+
+****Model:**** 
+
+As described in (McMahan, 2017): _Communication-Efficient Learning of Deep Networks from Decentralized Data_ ([arxiv.org/abs/1602.05629](https://arxiv.org/abs/1602.05629))
+
+|     | Layer     | Input Shape  | Output Shape | Param #   | Kernel Shape |
+|-----|-----------|--------------|--------------|-----------|--------------|
+| Net |           | [1, 28, 28]  | [10]         | --        | --           |
+|     | Conv2d    | [1, 28, 28]  | [32, 26, 26] | 832       | [5, 5]       |
+|     | MaxPool2d | [32, 26, 26] | [32, 14, 14] | --        | [2, 2]       |
+|     | Conv2d    | [32, 14, 14] | [64, 12, 12] | 51,264    | [5, 5]       |
+|     | MaxPool2d | [64, 12, 12] | [64, 7, 7]   | --        | [2, 2]       |
+|     | Linear    | [3136]       | [512]        | 1,606,144 | --           |
+|     | Linear    | [512]        | [10]         | 5,130     | --           |
+
+Total trainable params: 1,663,370
+
+****Dataset:**** By default, training split of MNIST dataset is divided in iid fashion across all 1000 clients, while test split stays on the server for centralized evaluation. Training dataset can also be divided using power law by setting `dataset.iid` to `False` in `base.yaml` config.
+
+****Training Hyperparameters:**** 
+
+| Hyperparameter             | Description                                                                                                                                                                                                                                                 | Default value |
+|----------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|
+| `server.clients_per_round` | Number of active/participating clients each round.                                                                                                                                                                                                          | 10            |
+| `server.num_clients`       | Number of total clients.                                                                                                                                                                                                                                    | 1000          |
+| `server_num_rounds`        | Number of training rounds, this does not include local training epochs.                                                                                                                                                                                     | 35            |
+| `server.s`                 | This describes the level of sparsity in the compression mask, needs to be between 2 and `server.clients_per_round`.                                                                                                                                         | 4             |
+| `server.p`                 | Describes the probability of server communication while doing local training, in other words, clients will in expectation do 1/`server.p` local epochs. Number of local epochs each rounds is synchronized across clients.                                  | 0.333         |
+| `server.uplink_factor`     | Weight of uplink (client to server) communication when calculating communication complexity.                                                                                                                                                                | 1             |
+| `server.downlink_factor`   | Weight of downlink (server to client) communication when calculating communication complexity.                                                                                                                                                              | 1             |
+| `client.learning_rate`     | Learning rate for client local training.                                                                                                                                                                                                                    | 0.01          |
+| `client.eta`               | Learning rate for updating control variates, needs to be between `server.p`/2 and `server.p` * (`server.clients_per_round` * (`server.s` - 1))/(`server.s` * (`server.clients_per_round` - 1)). Usually works very well when simply set to the upper bound. | 0.246         |
+| `meta.n_repeats`           | How many times should the training be repeated from the beginning for both Tamuna and FedAvg. Values bigger than 1 will produce plots that show how the randomness affects the algorithms.                                                                  | 3             |   
+
+## Environment Setup
+
+This requires `pyenv` and `poetry` already installed.
+
+```bash
+# set local python version via pyenv
+pyenv local 3.10.6
+# then fix that for poetry
+poetry env use 3.10.6
+# then install poetry env
+poetry install
+```
+
+
+## Running the Experiments
+
+Default experimental setup in defined in `conf/base.yaml`, this can be changed if needed.
+
+```bash
+poetry run python -m tamuna.main
+```
+
+Running time for default experimental setup is around 13min on Intel Core i5-12400F and Nvidia GeForce RTX 3060 Ti, 
+while the CPU-only version, which can be found in `conf/base-cpu.yaml`, takes around 20min.
+
+
+## Expected Results
+
+The resulting directory in `./outputs/` should contain (among other things) `communication_complexity.png` and `loss_accuracy.png`.
+
+<img src="_static/communication_complexity.png" width="500"/> <img src="_static/loss_accuracy.png" width="500"/>
diff --git a/baselines/tamuna/_static/communication_complexity.png b/baselines/tamuna/_static/communication_complexity.png
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diff --git a/baselines/tamuna/pyproject.toml b/baselines/tamuna/pyproject.toml
new file mode 100644
index 000000000000..fedcde9eb1c2
--- /dev/null
+++ b/baselines/tamuna/pyproject.toml
@@ -0,0 +1,141 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "tamuna"
+version = "1.0.0"
+description = "TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation"
+license = "Apache-2.0"
+authors = ["Ivan Agarský <agarskyivan@gmail.com>", "Grigory Malinovsky <grigorii.malinovskii@kaust.edu.sa>"]
+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/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl"}
+torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp310-cp310-linux_x86_64.whl"}
+torchaudio = { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2Bcu118-cp310-cp310-linux_x86_64.whl"}
+
+[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"
+
+[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/baselines/tamuna/tamuna/__init__.py b/baselines/tamuna/tamuna/__init__.py
new file mode 100644
index 000000000000..a5e567b59135
--- /dev/null
+++ b/baselines/tamuna/tamuna/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/tamuna/tamuna/client.py b/baselines/tamuna/tamuna/client.py
new file mode 100644
index 000000000000..8b85cf0c4026
--- /dev/null
+++ b/baselines/tamuna/tamuna/client.py
@@ -0,0 +1,236 @@
+"""Defines the MNIST Flower Client and a function to instantiate it."""
+# pylint: disable=too-many-instance-attributes,too-many-arguments
+import copy
+import os
+import pickle
+from collections import OrderedDict
+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 tamuna.models import fedavg_train, tamuna_train
+from tamuna.utils import apply_nn_compression
+
+
+class TamunaClient(fl.client.NumPyClient):
+    """Tamuna client for CNN training."""
+
+    STATE_DIR = "client_states"
+
+    def __init__(
+        self,
+        net: torch.nn.Module,
+        trainloader: DataLoader,
+        device: torch.device,
+        learning_rate: float,
+        cid: str,
+    ):
+        self.net = net
+        self.trainloader = trainloader
+        self.device = device
+        self.learning_rate = learning_rate
+        self.control_variate = None
+        self.old_compression_mask = None
+        self.old_compressed_net = None
+        self.cid = cid
+
+        self.state_file_name = f"{TamunaClient.STATE_DIR}/{self.cid}_state.bin"
+        self.__create_state()
+
+    def __create_state(self):
+        """Create client state."""
+        if not os.path.exists(self.state_file_name):
+            with open(self.state_file_name, "wb") as handle:
+                state = (
+                    self.control_variate,
+                    self.old_compression_mask,
+                    self.old_compressed_net,
+                )
+                pickle.dump(state, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+    def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
+        """Return the parameters of the current net."""
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def set_parameters(self, parameters: NDArrays) -> None:
+        """Change the parameters of the model using the given ones."""
+        params_dict = zip(self.net.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        self.net.load_state_dict(state_dict, strict=True)
+
+    def fit(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[NDArrays, int, Dict[str, Scalar]]:
+        """Distributed fit function for a given client."""
+        self.set_parameters(parameters)
+
+        mask = config["mask"]
+        if isinstance(mask, torch.Tensor):
+            mask = mask.to(self.device)
+        else:
+            raise TypeError("Mask must be a Tensor.")
+
+        self.__load_state()
+
+        self.net, self.control_variate = tamuna_train(
+            self.net,
+            self.trainloader,
+            self.device,
+            epochs=int(config["epochs"]),
+            eta=float(config["eta"]),
+            control_variate=self.control_variate,
+            old_compression_mask=self.old_compression_mask,
+            old_compressed_net=self.old_compressed_net,
+            server_net=copy.deepcopy(self.net),
+            learning_rate=self.learning_rate,
+        )
+
+        self.net = apply_nn_compression(self.net, mask)
+
+        self.__save_state(mask)
+
+        return self.get_parameters({}), len(self.trainloader), {}
+
+    def __save_state(self, mask):
+        """Save client state."""
+        with open(self.state_file_name, "wb") as handle:
+            state = (
+                self.control_variate,
+                mask,
+                self.net,
+            )
+            pickle.dump(state, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+    def __load_state(self):
+        """Load client state."""
+        with open(self.state_file_name, "rb") as handle:
+            state = pickle.load(handle)
+            (
+                self.control_variate,
+                self.old_compression_mask,
+                self.old_compressed_net,
+            ) = state
+
+
+class FedAvgClient(fl.client.NumPyClient):
+    """FedAvg client for CNN training."""
+
+    def __init__(
+        self,
+        net: torch.nn.Module,
+        trainloader: DataLoader,
+        device: torch.device,
+        learning_rate: float,
+        cid: int,
+    ) -> None:
+        super().__init__()
+        self.trainloader = trainloader
+        self.device = device
+        self.learning_rate = learning_rate
+        self.cid = cid
+        self.net = net
+
+    def set_parameters(self, parameters):
+        """Change the parameters of the model using the given ones."""
+        params_dict = zip(self.net.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        self.net.load_state_dict(state_dict, strict=True)
+
+    def get_parameters(self, config: Dict[str, Scalar]):
+        """Return the parameters of the current net."""
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def fit(self, parameters, config):
+        """Distributed fit function for a given client."""
+        self.set_parameters(parameters)
+        self.net = fedavg_train(
+            self.net,
+            self.trainloader,
+            epochs=config["epochs"],
+            learning_rate=self.learning_rate,
+            device=self.device,
+        )
+        return self.get_parameters({}), len(self.trainloader), {}
+
+
+def gen_tamuna_client_fn(
+    trainloaders: List[DataLoader],
+    learning_rate: float,
+    model: DictConfig,
+) -> Callable[[str], TamunaClient]:
+    """Generate the client function that creates Tamuna clients.
+
+    Parameters
+    ----------
+    trainloaders: List[DataLoader]
+        A list of DataLoaders, each pointing to the dataset training partition
+        belonging to a particular client.
+    learning_rate : float
+        The learning rate for Tamuna optimizer for clients.
+    model: DictConfig
+        Architecture of the model being instantiated
+
+    Returns
+    -------
+    Callable[[str], FlowerClient]
+        A tuple containing the client function that creates Tamuna clients
+    """
+
+    def tamuna_client_fn(cid: str) -> TamunaClient:
+        """Create a Tamuna client."""
+        # Load model
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        net = instantiate(model).to(device)
+
+        # Note: each client gets a different trainloader, so each client
+        # will train on their own unique data
+        trainloader = trainloaders[int(cid)]
+
+        return TamunaClient(net, trainloader, device, learning_rate, cid)
+
+    return tamuna_client_fn
+
+
+def gen_fedavg_client_fn(
+    trainloaders: List[DataLoader],
+    learning_rate: float,
+    model: DictConfig,
+) -> Callable[[str], FedAvgClient]:
+    """Generate the client function that creates FedAvg clients.
+
+    Parameters
+    ----------
+    trainloaders: List[DataLoader]
+        A list of DataLoaders, each pointing to the dataset training partition
+        belonging to a particular client.
+    learning_rate : float
+        The learning rate for the SGD optimizer for clients.
+    model: DictConfig
+        Architecture of the model being instantiated
+
+    Returns
+    -------
+    Callable[[str], FlowerClient]
+        A tuple containing the client function that creates FedAvg clients
+    """
+
+    def fedavg_client_fn(cid: str) -> FedAvgClient:
+        """Create a FedAvg client."""
+        # Load model
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        net = instantiate(model).to(device)
+
+        # Note: each client gets a different trainloader, so each client
+        # will train on their own unique data
+        trainloader = trainloaders[int(cid)]
+
+        return FedAvgClient(
+            net, trainloader, device, learning_rate=learning_rate, cid=int(cid)
+        )
+
+    return fedavg_client_fn
diff --git a/baselines/tamuna/tamuna/conf/base-cpu.yaml b/baselines/tamuna/tamuna/conf/base-cpu.yaml
new file mode 100644
index 000000000000..59a00c0333ab
--- /dev/null
+++ b/baselines/tamuna/tamuna/conf/base-cpu.yaml
@@ -0,0 +1,34 @@
+meta:
+  n_repeats: 3
+  seed: 42
+
+dataset:
+  iid: True
+
+model:
+  _target_: tamuna.models.Net
+  num_classes: 10
+
+strategy:
+  _target_: tamuna.strategy.TamunaStrategy
+
+server:
+  clients_per_round: 10
+  num_clients: 1000
+  num_rounds: 35
+  # sparsity parameter, 2 <= s <= clients_per_round
+  s: 4
+  # probability of server communication, i.e. clients will in expectation do 1 / p local epochs
+  # number of local epochs each round is synchronized across the clients
+  p: 0.333
+  server_device: 'cpu'
+  uplink_factor: 1
+  downlink_factor: 1
+
+client:
+  client_resources:
+    num_cpus: 2
+    num_gpus: 0
+  learning_rate: 0.01
+  # eta should be between p / 2 and p * [(clients_per_round * (s - 1))/(s * (clients_per_round - 1))]
+  eta: 0.246
diff --git a/baselines/tamuna/tamuna/conf/base.yaml b/baselines/tamuna/tamuna/conf/base.yaml
new file mode 100644
index 000000000000..c4a5b3bc8c67
--- /dev/null
+++ b/baselines/tamuna/tamuna/conf/base.yaml
@@ -0,0 +1,34 @@
+meta:
+  n_repeats: 3
+  seed: 42
+
+dataset:
+  iid: True
+
+model:
+  _target_: tamuna.models.Net
+  num_classes: 10
+
+strategy:
+  _target_: tamuna.strategy.TamunaStrategy
+
+server:
+  clients_per_round: 10
+  num_clients: 1000
+  num_rounds: 35
+  # sparsity parameter, 2 <= s <= clients_per_round
+  s: 4
+  # probability of server communication, i.e. clients will in expectation do 1 / p local epochs
+  # number of local epochs each round is synchronized across the clients
+  p: 0.333
+  server_device: 'cuda:0'
+  uplink_factor: 1
+  downlink_factor: 1
+
+client:
+  client_resources:
+    num_cpus: 2
+    num_gpus: 0.2
+  learning_rate: 0.01
+  # eta should be between p / 2 and p * [(clients_per_round * (s - 1))/(s * (clients_per_round - 1))]
+  eta: 0.246
diff --git a/baselines/tamuna/tamuna/dataset.py b/baselines/tamuna/tamuna/dataset.py
new file mode 100644
index 000000000000..7208a788e069
--- /dev/null
+++ b/baselines/tamuna/tamuna/dataset.py
@@ -0,0 +1,34 @@
+"""Load the MNIST dataset into train and test loaders."""
+from typing import List, Optional, Tuple
+
+from torch.utils.data import DataLoader
+
+from tamuna.dataset_preparation import partition_data
+
+
+def load_datasets(
+    num_clients: int, iid: bool = False, seed: Optional[int] = 42
+) -> Tuple[List[DataLoader], DataLoader]:
+    """Create dataloaders.
+
+    Parameters
+    ----------
+    num_clients : int
+        The number of clients that hold a part of the data
+    iid : bool
+        Whether the data should be split in independent identically distributed (iid)
+        fashion or not. If False, data will be split according to power law.
+    seed : int, optional
+        Used to set a fix seed to replicate experiments, by default 42
+
+    Returns
+    -------
+    Tuple[List[DataLoader], DataLoader]
+        The DataLoaders for training, the DataLoader for testing.
+    """
+    datasets, testset = partition_data(num_clients, iid, seed=seed)
+
+    trainloaders = []
+    for dataset in datasets:
+        trainloaders.append(DataLoader(dataset, batch_size=len(dataset), shuffle=True))
+    return trainloaders, DataLoader(testset, batch_size=len(testset))
diff --git a/baselines/tamuna/tamuna/dataset_preparation.py b/baselines/tamuna/tamuna/dataset_preparation.py
new file mode 100644
index 000000000000..dc9e14b43dcf
--- /dev/null
+++ b/baselines/tamuna/tamuna/dataset_preparation.py
@@ -0,0 +1,200 @@
+"""Functions to download and partition the MNIST dataset."""
+
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from torch.utils.data import ConcatDataset, Dataset, Subset, random_split
+from torchvision.datasets import MNIST
+
+
+def download_data() -> Tuple[Dataset, Dataset]:
+    """Download MNIST.
+
+    Returns
+    -------
+    Tuple[MNIST, MNIST]
+        The dataset for training and the dataset for testing MNIST.
+    """
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+    )
+    trainset = MNIST("./dataset", train=True, download=True, transform=transform)
+    testset = MNIST("./dataset", train=False, download=True, transform=transform)
+    return trainset, testset
+
+
+def sort_by_class(
+    trainset: Dataset,
+) -> Dataset:
+    """Sort dataset by class/label.
+
+    Parameters
+    ----------
+    trainset : Dataset
+        The training dataset that needs to be sorted.
+
+    Returns
+    -------
+    Dataset
+        The sorted training dataset.
+    """
+    class_counts = np.bincount(trainset.targets)
+    idxs = trainset.targets.argsort()  # sort targets in ascending order
+
+    tmp = []  # create subset of smallest class
+    tmp_targets = []  # same for targets
+
+    start = 0
+    for count in np.cumsum(class_counts):
+        tmp.append(
+            Subset(trainset, idxs[start : int(count + start)])
+        )  # add rest of classes
+        tmp_targets.append(trainset.targets[idxs[start : int(count + start)]])
+        start += count
+    sorted_dataset = ConcatDataset(tmp)  # concat dataset
+    sorted_dataset.targets = torch.cat(tmp_targets)  # concat targets
+    return sorted_dataset
+
+
+def partition_data(
+    num_clients: int, iid: bool, seed: Optional[int] = 42
+) -> Tuple[List[Dataset], Dataset]:
+    """Split training set into iid partitions or power law split.
+
+    Parameters
+    ----------
+    num_clients : int
+        The number of clients that hold a part of the data
+    iid : bool
+        Whether the data should be split in independent identically distributed (iid)
+        fashion or not. If False, data will be split according to power law.
+    seed : int, optional
+        Used to set a fix seed to replicate experiments, by default 42
+
+    Returns
+    -------
+    Tuple[List[Dataset], Dataset]
+        A list of dataset for each client and a single dataset to be used
+        for testing the model.
+    """
+    trainset, testset = download_data()
+
+    partition_size = int(len(trainset) / num_clients)
+    lengths = [partition_size] * num_clients
+
+    if iid:
+        datasets = random_split(trainset, lengths, torch.Generator().manual_seed(seed))
+    else:
+        trainset_sorted = sort_by_class(trainset)
+        datasets = power_law_split(
+            trainset_sorted,
+            num_partitions=num_clients,
+            num_labels_per_partition=2,
+            min_data_per_partition=10,
+            mean=0.0,
+            sigma=2.0,
+        )
+
+    return datasets, testset
+
+
+# pylint: disable=too-many-arguments,too-many-locals
+def power_law_split(
+    sorted_trainset: Dataset,
+    num_partitions: int,
+    num_labels_per_partition: int = 2,
+    min_data_per_partition: int = 10,
+    mean: float = 0.0,
+    sigma: float = 2.0,
+) -> List[Dataset]:
+    """Partitions the dataset following a power-law distribution.
+
+    It follows the implementation of Li et al 2020: https://arxiv.org/abs/1812.06127
+    with default values set accordingly.
+
+    Parameters
+    ----------
+    sorted_trainset : Dataset
+        The training dataset sorted by label/class.
+    num_partitions: int
+        Number of partitions to create
+    num_labels_per_partition: int
+        Number of labels to have in each dataset partition. For
+        example if set to two, this means all training examples in
+        a given partition will belong to the same two classes. default 2
+    min_data_per_partition: int
+        Minimum number of datapoints included in each partition, default 10
+    mean: float
+        Mean value for LogNormal distribution to construct power-law, default 0.0
+    sigma: float
+        Sigma value for LogNormal distribution to construct power-law, default 2.0
+
+    Returns
+    -------
+    Dataset
+        The partitioned training dataset.
+    """
+    targets = sorted_trainset.targets
+    full_idx = range(len(targets))
+
+    class_counts = np.bincount(sorted_trainset.targets)
+    labels_cs = np.cumsum(class_counts)
+    labels_cs = [0] + labels_cs[:-1].tolist()
+
+    partitions_idx: List[List[int]] = []
+    num_classes = len(np.bincount(targets))
+    hist = np.zeros(num_classes, dtype=np.int32)
+
+    # assign min_data_per_partition
+    min_data_per_class = int(min_data_per_partition / num_labels_per_partition)
+    for u_id in range(num_partitions):
+        partitions_idx.append([])
+        for cls_idx in range(num_labels_per_partition):
+            # label for the u_id-th client
+            cls = (u_id + cls_idx) % num_classes
+            # record minimum data
+            indices = list(
+                full_idx[
+                    labels_cs[cls]
+                    + hist[cls] : labels_cs[cls]
+                    + hist[cls]
+                    + min_data_per_class
+                ]
+            )
+            partitions_idx[-1].extend(indices)
+            hist[cls] += min_data_per_class
+
+    # add remaining images following power-law
+    probs = np.random.lognormal(
+        mean,
+        sigma,
+        (num_classes, int(num_partitions / num_classes), num_labels_per_partition),
+    )
+    remaining_per_class = class_counts - hist
+    # obtain how many samples each partition should be assigned for each of
+    # the labels it contains
+    probs = (
+        remaining_per_class.reshape((-1, 1, 1))
+        * probs
+        / np.sum(probs, (1, 2), keepdims=True)
+    )
+
+    for u_id in range(num_partitions):
+        for cls_idx in range(num_labels_per_partition):
+            cls = (u_id + cls_idx) % num_classes
+            count = int(probs[cls, u_id // num_classes, cls_idx])
+
+            # add count of specific class to partition
+            indices = list(
+                full_idx[
+                    labels_cs[cls] + hist[cls] : labels_cs[cls] + hist[cls] + count
+                ]
+            )
+            partitions_idx[u_id].extend(indices)
+            hist[cls] += count
+
+    # construct subsets
+    partitions = [Subset(sorted_trainset, p) for p in partitions_idx]
+    return partitions
diff --git a/baselines/tamuna/tamuna/main.py b/baselines/tamuna/tamuna/main.py
new file mode 100644
index 000000000000..f9c4eff4aa72
--- /dev/null
+++ b/baselines/tamuna/tamuna/main.py
@@ -0,0 +1,211 @@
+"""Run CNN federated learning for MNIST dataset."""
+import os
+from typing import Dict, List
+
+import flwr as fl
+import hydra
+import numpy as np
+import torch.random
+from flwr.common import Scalar
+from flwr.server.history import History
+from flwr.server.strategy import FedAvg
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig, OmegaConf
+from torch.utils.data import DataLoader
+
+import tamuna.client as client
+import tamuna.server as server
+from tamuna.dataset import load_datasets
+from tamuna.strategy import TamunaStrategy
+from tamuna.utils import compare_histories, save_results_as_pickle
+
+
+@hydra.main(config_path="conf", config_name="base", version_base=None)
+def main(cfg: DictConfig) -> None:
+    """Run CNN federated learning on MNIST.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    # print config structured as YAML
+    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
+    with open(f"{save_path}/config.yaml", "wt") as handle:
+        OmegaConf.save(cfg, handle)
+
+    # partition dataset and get dataloaders
+    trainloaders, testloader = load_datasets(
+        num_clients=cfg.server.num_clients, iid=cfg.dataset.iid
+    )
+
+    tamuna_histories = run_tamuna(cfg, save_path, testloader, trainloaders)
+    fedavg_histories = run_fedavg(cfg, save_path, testloader, trainloaders)
+
+    with open("model_dim.txt", "rt") as handle:
+        dim = int(handle.readline())
+
+    compare_histories(tamuna_histories, fedavg_histories, dim, save_path, cfg)
+
+
+def run_fedavg(
+    cfg: DictConfig,
+    save_path: str,
+    testloader: DataLoader,
+    trainloaders: List[DataLoader],
+) -> List[History]:
+    """Run FedAvg.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    save_path: str
+         Path where to save the results of the training.
+    testloader: DataLoader
+        Dataloader contaning test split for centralized evaluation
+    trainloaders: List[DataLoader]
+        Dataloaders for clients
+
+    Returns
+    -------
+    List[History]
+        Histories of every run (based of n_repeats).
+    """
+    # prepare function that will be used to spawn each client
+    client_fn = client.gen_fedavg_client_fn(
+        trainloaders=trainloaders,
+        learning_rate=cfg.client.learning_rate,
+        model=cfg.model,
+    )
+
+    # pylint: disable=unused-argument
+    def configure_fit_fn(server_round: int) -> Dict[str, Scalar]:
+        local_epochs: Scalar = int(1 / cfg.server.p)
+        config = {"epochs": local_epochs}
+        return config
+
+    # using only central evaluation
+    evaluate_fn = server.gen_evaluate_fn(
+        testloader, device=cfg.server.server_device, model=cfg.model
+    )
+
+    histories = []
+    for i in range(cfg.meta.n_repeats):
+        np.random.seed(cfg.meta.seed)
+        torch.manual_seed(cfg.meta.seed)
+
+        # Start simulation
+        history = fl.simulation.start_simulation(
+            client_fn=client_fn,
+            num_clients=cfg.server.num_clients,
+            config=fl.server.ServerConfig(num_rounds=cfg.server.num_rounds),
+            client_resources={
+                "num_cpus": cfg.client.client_resources.num_cpus,
+                "num_gpus": cfg.client.client_resources.num_gpus,
+            },
+            strategy=FedAvg(
+                fraction_fit=cfg.server.clients_per_round / cfg.server.num_clients,
+                fraction_evaluate=0,
+                min_evaluate_clients=0,
+                on_fit_config_fn=configure_fit_fn,
+                evaluate_fn=evaluate_fn,
+            ),
+        )
+
+        # save results as a Python pickle using a file_path
+        # the directory created by Hydra
+        save_results_as_pickle(history, file_path=f"{save_path}/fedavg_results_{i}.pkl")
+
+        histories.append(history)
+
+    return histories
+
+
+def run_tamuna(
+    cfg: DictConfig,
+    save_path: str,
+    testloader: DataLoader,
+    trainloaders: List[DataLoader],
+):
+    """Run Tamuna.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    save_path: str
+         Path where to save the results of the training.
+    testloader: DataLoader
+        Dataloader contaning test split for centralized evaluation
+    trainloaders: List[DataLoader]
+        Dataloaders for clients
+
+    Returns
+    -------
+    List[History]
+        Histories of every run (based of n_repeats).
+    """
+    # prepare function that will be used to spawn each client
+    client_fn = client.gen_tamuna_client_fn(
+        trainloaders=trainloaders,
+        learning_rate=cfg.client.learning_rate,
+        model=cfg.model,
+    )
+
+    # using only central evaluation
+    evaluate_fn = server.gen_evaluate_fn(
+        testloader, device=cfg.server.server_device, model=cfg.model
+    )
+
+    histories = []
+    for i in range(cfg.meta.n_repeats):
+        np.random.seed(cfg.meta.seed)
+        torch.manual_seed(cfg.meta.seed)
+
+        # remove possible previous client states
+        if os.path.exists(client.TamunaClient.STATE_DIR):
+            for filename in os.listdir(client.TamunaClient.STATE_DIR):
+                if filename.endswith("_state.bin"):
+                    os.remove(f"{client.TamunaClient.STATE_DIR}/{filename}")
+        else:
+            os.mkdir(client.TamunaClient.STATE_DIR)
+
+        # number of epochs per round is determined by probability p
+        epochs_per_round = np.random.geometric(
+            p=cfg.server.p, size=cfg.server.num_rounds
+        ).tolist()
+
+        # Start simulation
+        history = fl.simulation.start_simulation(
+            client_fn=client_fn,
+            num_clients=cfg.server.num_clients,
+            config=fl.server.ServerConfig(num_rounds=cfg.server.num_rounds),
+            client_resources={
+                "num_cpus": cfg.client.client_resources.num_cpus,
+                "num_gpus": cfg.client.client_resources.num_gpus,
+            },
+            strategy=TamunaStrategy(
+                clients_per_round=cfg.server.clients_per_round,
+                epochs_per_round=epochs_per_round,
+                eta=cfg.client.eta,
+                sparsity=cfg.server.s,
+                evaluate_fn=evaluate_fn,
+            ),
+        )
+
+        # save results as a Python pickle using a file_path
+        # the directory created by Hydra
+        save_results_as_pickle(history, file_path=f"{save_path}/tamuna_results_{i}.pkl")
+
+        histories.append(history)
+
+    return histories
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/tamuna/tamuna/models.py b/baselines/tamuna/tamuna/models.py
new file mode 100644
index 000000000000..58d506d86df2
--- /dev/null
+++ b/baselines/tamuna/tamuna/models.py
@@ -0,0 +1,328 @@
+"""CNN model architecture, training, and testing functions for MNIST."""
+# pylint: disable=too-many-arguments
+
+import copy
+from collections import OrderedDict
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+
+from tamuna.utils import apply_nn_compression
+
+
+class Net(nn.Module):
+    """Convolutional Neural Network architecture.
+
+    As described in McMahan 2017 paper :
+
+    [Communication-Efficient Learning of Deep Networks from
+    Decentralized Data] (https://arxiv.org/pdf/1602.05629.pdf)
+    """
+
+    def __init__(self, num_classes: int = 10) -> None:
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 32, 5, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, 5, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=(2, 2), padding=1)
+        self.fc1 = nn.Linear(64 * 7 * 7, 512)
+        self.fc2 = nn.Linear(512, num_classes)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward pass of the CNN.
+
+        Parameters
+        ----------
+        x : torch.Tensor
+            Input Tensor that will pass through the network
+
+        Returns
+        -------
+        torch.Tensor
+            The resulting Tensor after it has passed through the network
+        """
+        x = F.relu(self.conv1(x))
+        x = self.pool(x)
+        x = F.relu(self.conv2(x))
+        x = self.pool(x)
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+
+def __model_zeroed_out(net: nn.Module):
+    """Return network with all the weights zeroed-out.
+
+    Parameters
+    ----------
+    net : nn.Module
+        Model to be zeroed-out.
+    """
+    control_variate = copy.deepcopy(net)
+    state_dict = OrderedDict(
+        {k: torch.zeros_like(v) for k, v in net.state_dict().items()}
+    )
+    control_variate.load_state_dict(state_dict, strict=True)
+    return control_variate
+
+
+def tamuna_train(
+    net: nn.Module,
+    trainloader: DataLoader,
+    device: torch.device,
+    epochs: int,
+    learning_rate: float,
+    eta: float,
+    server_net: nn.Module,
+    control_variate: nn.Module,
+    old_compression_mask: torch.tensor,
+    old_compressed_net: nn.Module,
+) -> Tuple[nn.Module, nn.Module]:
+    """Train the network on the training set.
+
+    Parameters
+    ----------
+    net : nn.Module
+        The neural network to train.
+    trainloader : DataLoader
+        The DataLoader containing the data to train the network on.
+    device : torch.device
+        The device on which the model should be trained, either 'cpu' or 'cuda'.
+    epochs : int
+        The number of epochs the model should be trained for.
+    learning_rate: float
+        Learning rate to be used.
+    eta: float
+        TAMUNA hyperparameter used during training.
+    server_net: nn.Module
+        Current server model.
+    control_variate: nn.Module
+        Current control variate for this client.
+    old_compression_mask: torch.tensor
+        Previous compression vector for this client.
+    old_compressed_net: nn.Module
+        Compressed model that was sent to the server from this client
+        in the previous round.
+    """
+    criterion = torch.nn.CrossEntropyLoss()
+    net.train()
+
+    if control_variate is not None:
+        with torch.no_grad():
+            control_variate.to(device)
+            __update_control_variate(
+                control_variate,
+                eta,
+                learning_rate,
+                old_compressed_net,
+                old_compression_mask,
+                server_net,
+            )
+    else:
+        control_variate = __model_zeroed_out(net)
+
+    for _ in range(epochs):
+        net = __tamuna_train_one_epoch(
+            net,
+            trainloader,
+            device,
+            criterion,
+            learning_rate,
+            control_variate,
+        )
+
+    return net, control_variate
+
+
+def fedavg_train(
+    net: torch.nn.Module,
+    trainloader: DataLoader,
+    epochs: int,
+    learning_rate: float,
+    device: torch.device,
+):
+    """Train the network on the training set.
+
+    Parameters
+    ----------
+    net : nn.Module
+        The neural network to train.
+    trainloader : DataLoader
+        The DataLoader containing the data to train the network on.
+    epochs : int
+        The number of epochs the model should be trained for.
+    learning_rate: float
+        Learning rate to be used.
+    device : torch.device
+        The device on which the model should be trained, either 'cpu' or 'cuda'.
+    """
+    criterion = torch.nn.CrossEntropyLoss()
+    optim = torch.optim.SGD(net.parameters(), lr=learning_rate)
+    for _ in range(epochs):
+        for images, labels in trainloader:
+            images, labels = images.to(device), labels.to(device)
+            optim.zero_grad()
+            loss = criterion(net(images), labels)
+            loss.backward()
+            optim.step()
+    return net
+
+
+def __update_control_variate(
+    control_variate: nn.Module,
+    eta: float,
+    learning_rate: float,
+    old_compressed_net: nn.Module,
+    old_compression_mask: torch.tensor,
+    server_net: nn.Module,
+):
+    """Update the control variate for current client.
+
+    Parameters
+    ----------
+    control_variate: nn.Module
+        Current control variate for this client.
+    eta: float
+        TAMUNA hyperparameter used during training.
+    learning_rate: float
+        Learning rate to be used.
+    old_compressed_net: nn.Module
+        Compressed model that was sent to the server from this client
+        in the previous round.
+    old_compression_mask: torch.tensor
+        Previous compression vector for this client.
+    server_net: nn.Module
+        Current server model.
+    """
+    old_compressed_modules = []
+    for module in list(old_compressed_net.modules())[1:]:
+        if len(list(module.parameters())) != 0:
+            old_compressed_modules.append(module)
+
+    server_net = apply_nn_compression(server_net, old_compression_mask)
+    server_modules = []
+    for server_module in list(server_net.modules())[1:]:
+        if len(list(server_module.parameters())) != 0:
+            server_modules.append(server_module)
+
+    control_variate_modules = []
+    for control_variate_module in list(control_variate.modules())[1:]:
+        if len(list(control_variate_module.parameters())) != 0:
+            control_variate_modules.append(control_variate_module)
+
+    for i, module in enumerate(control_variate_modules):
+        module.weight.copy_(
+            module.weight.data
+            + (eta / learning_rate)
+            * (server_modules[i].weight.data - old_compressed_modules[i].weight.data)
+        )
+        module.bias.copy_(
+            module.bias.data
+            + (eta / learning_rate)
+            * (server_modules[i].bias.data - old_compressed_modules[i].bias.data)
+        )
+
+
+def __tamuna_train_one_epoch(
+    net: nn.Module,
+    trainloader: DataLoader,
+    device: torch.device,
+    criterion: torch.nn.CrossEntropyLoss,
+    learning_rate: float,
+    control_variate: nn.Module,
+) -> nn.Module:
+    """Train for one epoch.
+
+    Parameters
+    ----------
+    net : nn.Module
+        The neural network to train.
+    trainloader : DataLoader
+        The DataLoader containing the data to train the network on.
+    device : torch.device
+        The device on which the model should be trained, either 'cpu' or 'cuda'.
+    criterion : torch.nn.CrossEntropyLoss
+        The loss function to use for training
+    learning_rate : float
+        Learning rate to be used.
+    control_variate: nn.Module
+        Control variate for this client.
+
+    Returns
+    -------
+    nn.Module
+        The model that has been trained for one epoch.
+    """
+    for images, labels in trainloader:
+        images, labels = images.to(device), labels.to(device)
+
+        net.zero_grad(set_to_none=False)
+
+        loss = criterion(net(images), labels)
+        loss.backward()
+
+        with torch.no_grad():
+            control_variate.to(device)
+
+            modules = []
+            for module in list(net.modules())[1:]:
+                if len(list(module.parameters())) != 0:
+                    modules.append(module)
+
+            control_variate_modules = []
+            for control_variate_module in list(control_variate.modules())[1:]:
+                if len(list(control_variate_module.parameters())) != 0:
+                    control_variate_modules.append(control_variate_module)
+
+            for i, module in enumerate(modules):
+                module.weight.copy_(
+                    module.weight.data
+                    - learning_rate
+                    * (module.weight.grad.data - control_variate_modules[i].weight.data)
+                )
+                module.bias.copy_(
+                    module.bias.data
+                    - learning_rate
+                    * (module.bias.grad.data - control_variate_modules[i].bias.data)
+                )
+    return net
+
+
+def test(
+    net: nn.Module, testloader: DataLoader, device: torch.device
+) -> Tuple[float, float]:
+    """Evaluate the network on the entire test set.
+
+    Parameters
+    ----------
+    net : nn.Module
+        The neural network to test.
+    testloader : DataLoader
+        The DataLoader containing the data to test the network on.
+    device : torch.device
+        The device on which the model should be tested, either 'cpu' or 'cuda'.
+
+    Returns
+    -------
+    Tuple[float, float]
+        The loss and the accuracy of the input model on the given data.
+    """
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, total, loss = 0, 0, 0.0
+    net.eval()
+    with torch.no_grad():
+        for images, labels in testloader:
+            images, labels = images.to(device), labels.to(device)
+            outputs = net(images)
+            loss += criterion(outputs, labels).item()
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+    if len(testloader.dataset) == 0:
+        raise ValueError("Testloader can't be 0, exiting...")
+    loss /= len(testloader.dataset)
+    accuracy = correct / total
+    return loss, accuracy
diff --git a/baselines/tamuna/tamuna/server.py b/baselines/tamuna/tamuna/server.py
new file mode 100644
index 000000000000..057fa339a985
--- /dev/null
+++ b/baselines/tamuna/tamuna/server.py
@@ -0,0 +1,53 @@
+"""Centralized server evaluation function generation."""
+
+from collections import OrderedDict
+from typing import Callable, Dict, Optional, Tuple
+
+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 tamuna.models import test
+
+
+def gen_evaluate_fn(
+    testloader: DataLoader,
+    device: torch.device,
+    model: DictConfig,
+) -> Callable[
+    [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]
+]:
+    """Generate the function for centralized evaluation.
+
+    Parameters
+    ----------
+    testloader : DataLoader
+        The dataloader to test the model with.
+    device : torch.device
+        The device to test the model on.
+    model: DictConfig
+        Architecture of the model being evaluated.
+
+    Returns
+    -------
+    Callable[[NDArrays], Tuple[float, Dict[str, Scalar]]]
+        The centralized evaluation function.
+    """
+
+    # pylint: disable=unused-argument
+    def evaluate(
+        server_round: int, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Use the entire MNIST test set for evaluation."""
+        net = instantiate(model)
+        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)
+        net.to(device)
+
+        loss, accuracy = test(net, testloader, device=device)
+        return loss, {"accuracy": accuracy}
+
+    return evaluate
diff --git a/baselines/tamuna/tamuna/strategy.py b/baselines/tamuna/tamuna/strategy.py
new file mode 100644
index 000000000000..3a0e155f2a06
--- /dev/null
+++ b/baselines/tamuna/tamuna/strategy.py
@@ -0,0 +1,120 @@
+"""Tamuna and FedAvg strategies."""
+
+from typing import Callable, List
+
+import flwr.common
+import numpy as np
+import torch
+from flwr.common import FitIns, NDArrays, ndarrays_to_parameters, parameters_to_ndarrays
+from flwr.server.strategy import Strategy
+
+from tamuna.models import Net
+
+
+def aggregate(weights: List[NDArrays], sparsity: float) -> NDArrays:
+    """Compute average of the clients' weights."""
+    averaged_weights = [
+        np.sum(layer_updates, axis=0) / sparsity for layer_updates in zip(*weights)
+    ]
+    return averaged_weights
+
+
+def create_pattern(dim: int, cohort_size: int, sparsity: int):
+    """Create compression pattern."""
+    pattern = torch.zeros(size=(dim, cohort_size))
+    if dim >= cohort_size / sparsity:
+        k = 0
+        for i in range(dim):
+            for _ in range(sparsity):
+                pattern[i, k] = 1
+                k = (k + 1) % cohort_size
+    else:
+        k = 0
+        for _ in range(sparsity):
+            for i in range(dim):
+                pattern[i, k] = 1
+                k += 1
+
+    return pattern
+
+
+def shuffle_columns(pattern: torch.Tensor):
+    """Shuffle the columns of the compression pattern."""
+    pattern = pattern[:, torch.randperm(pattern.size()[1])]
+    return pattern
+
+
+class TamunaStrategy(Strategy):
+    """Tamuna Strategy with control variates and compression."""
+
+    # pylint: disable=too-many-instance-attributes,too-many-arguments
+    def __init__(
+        self,
+        clients_per_round: int,
+        epochs_per_round: List[int],
+        eta: float,
+        sparsity: int,
+        evaluate_fn: Callable,
+    ) -> None:
+        self.clients_per_round = clients_per_round
+        self.epochs_per_round = epochs_per_round
+        self.evaluate_fn = evaluate_fn
+        self.eta = eta
+        self.sparsity = sparsity
+        self.dim = None
+        self.server_model = None
+        self.compression_pattern = torch.zeros(size=(0, 0))
+
+    def initialize_parameters(self, client_manager):
+        """Initialize the server model."""
+        self.server_model = Net()
+        self.dim = sum(p.numel() for p in self.server_model.parameters())
+
+        with open("model_dim.txt", "wt") as handle:
+            handle.write(str(self.dim))
+
+        self.compression_pattern = create_pattern(
+            self.dim, self.clients_per_round, self.sparsity
+        )  # dim x cohort_size
+
+        ndarrays = [
+            val.cpu().numpy() for _, val in self.server_model.state_dict().items()
+        ]
+        return flwr.common.ndarrays_to_parameters(ndarrays)
+
+    def configure_fit(self, server_round: int, parameters, client_manager):
+        """Sample clients and create compression pattern for each of them."""
+        sampled_clients = client_manager.sample(self.clients_per_round)
+        client_fit_ins = []
+
+        self.compression_pattern = shuffle_columns(self.compression_pattern)
+
+        for i in range(self.clients_per_round):
+            config = {
+                "epochs": self.epochs_per_round[server_round - 1],
+                "eta": self.eta,
+                "mask": self.compression_pattern[:, i],
+            }
+            client_fit_ins.append(FitIns(parameters, config))
+
+        return [(client, client_fit_ins[i]) for i, client in enumerate(sampled_clients)]
+
+    def aggregate_fit(self, server_round, results, failures):
+        """Average the clients' weights."""
+        weights = [parameters_to_ndarrays(fit_res.parameters) for _, fit_res in results]
+        parameters_aggregated = ndarrays_to_parameters(
+            aggregate(weights, self.sparsity)
+        )
+        return parameters_aggregated, {}
+
+    def configure_evaluate(self, server_round, parameters, client_manager):
+        """Not used."""
+        return []
+
+    def aggregate_evaluate(self, server_round, results, failures):
+        """Not used."""
+        return None, {}
+
+    def evaluate(self, server_round: int, parameters):
+        """Centralized evaluation."""
+        return self.evaluate_fn(server_round, parameters_to_ndarrays(parameters), {})
diff --git a/baselines/tamuna/tamuna/utils.py b/baselines/tamuna/tamuna/utils.py
new file mode 100644
index 000000000000..73cf38ac007b
--- /dev/null
+++ b/baselines/tamuna/tamuna/utils.py
@@ -0,0 +1,217 @@
+"""Contains utility functions for CNN FL on MNIST."""
+
+import pickle
+from functools import reduce
+from pathlib import Path
+from typing import List
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from flwr.server.history import History
+from omegaconf import DictConfig
+from torch import nn
+
+
+def apply_nn_compression(net: nn.Module, mask: torch.tensor) -> nn.Module:
+    """Zero out some of the model weights based on compression mask.
+
+    Parameters
+    ----------
+    net : nn.Module
+        Model to be compressed.
+    mask: torch.Tensor
+        One dimensional binary vector having ones for weights that are preserved.
+    """
+    list_of_reshaped_layers = []
+    list_of_shapes = []
+
+    for layer in net.parameters():
+        reshaped_layer = torch.flatten(layer.data)
+        list_of_reshaped_layers.append(reshaped_layer)
+        shape = reduce((lambda x, y: x * y), list(layer.data.shape))
+        list_of_shapes.append(shape)
+    cat_full_vec = torch.cat(list_of_reshaped_layers)
+    compressed_full_vec = torch.mul(cat_full_vec, mask)
+
+    compressed_split_vec = torch.split(compressed_full_vec, list_of_shapes)
+
+    for i, layer in enumerate(net.parameters()):
+        layer.data = compressed_split_vec[i].reshape(layer.data.shape)
+
+    return net
+
+
+def save_results_as_pickle(
+    history: History,
+    file_path: str,
+) -> None:
+    """Save results from simulation using pickle.
+
+    Parameters
+    ----------
+    history: History
+        History returned by start_simulation.
+    file_path: str
+        Path to file to create and store history.
+    """
+    data = {"history": history}
+
+    # save results to pickle
+    with open(file_path, "wb") as handle:
+        pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+
+def compare_histories(
+    tamuna_histories: List[History],
+    fedavg_histories: List[History],
+    dim: int,
+    save_path: str,
+    cfg: DictConfig,
+):
+    """Compare Tamuna and FedAvg histories."""
+    compare_loss_and_accuracy(fedavg_histories, tamuna_histories, save_path)
+    compare_communication_complexity(
+        fedavg_histories, tamuna_histories, save_path, dim, cfg
+    )
+
+
+def compare_communication_complexity(
+    fedavg_histories: List[History],
+    tamuna_histories: List[History],
+    save_path: str,
+    dim: int,
+    cfg: DictConfig,
+):
+    """Compare Tamuna with FedAvg based on communication complexity."""
+    plot_histories(
+        tamuna_histories,
+        cfg,
+        dim,
+        np.ceil((cfg.server.s * dim) / cfg.server.clients_per_round),
+        "Tamuna",
+    )
+    plot_histories(fedavg_histories, cfg, dim, dim, "FedAvg")
+
+    plt.ylabel("Loss")
+    plt.yscale("log")
+    plt.xlabel("Communicated real numbers")
+    plt.legend()
+    plt.grid(visible=True, which="both", linewidth=0.5, alpha=0.5)
+    plt.minorticks_on()
+    plt.title("Communication complexity")
+    plt.tight_layout()
+    plt.savefig(Path(save_path) / Path("communication_complexity.png"), dpi=300)
+    plt.close()
+
+
+def plot_histories(
+    histories: List[History],
+    cfg: DictConfig,
+    down_complexity: int,
+    up_complexity: int,
+    label: str,
+):
+    """Plot multiple runs."""
+    losses_across_runs = []
+
+    for run in histories:
+        history_losses = run.losses_centralized
+        rounds, loss_values = zip(*history_losses)
+        losses_across_runs.append(loss_values)
+
+    x_axis = np.arange(len(rounds)) * (
+        cfg.server.uplink_factor * up_complexity
+        + cfg.server.downlink_factor * down_complexity
+    )
+    lowest_loss_across_runs = np.min(losses_across_runs, axis=0)
+    highest_loss_across_runs = np.max(losses_across_runs, axis=0)
+    avg_loss_across_runs = np.add(lowest_loss_across_runs, highest_loss_across_runs) / 2
+    plt.fill_between(
+        x_axis,
+        highest_loss_across_runs,
+        lowest_loss_across_runs,
+        alpha=0.4,
+        label=label,
+    )
+    plt.plot(x_axis, avg_loss_across_runs, linewidth=2)
+
+
+def compare_loss_and_accuracy(
+    fedavg_histories: List[History], tamuna_histories: List[History], save_path: str
+):
+    """Compare Tamuna with FedAvg based on loss and accuracy."""
+    _, axs = plt.subplots(nrows=2, ncols=1, sharex="row")
+
+    plot_histories_with_accuracy(axs, tamuna_histories, label="Tamuna")
+    plot_histories_with_accuracy(axs, fedavg_histories, label="FedAvg")
+
+    axs[0].set_title("MNIST Test Loss")
+    axs[0].set_ylabel("Loss")
+    axs[0].set_yscale("log")
+    axs[0].legend()
+    axs[0].grid(visible=True, which="both", linewidth=0.5, alpha=0.5)
+    axs[0].minorticks_on()
+    axs[0].set_xlabel("Rounds")
+
+    axs[1].set_title("MNIST Test Accuracy")
+    axs[1].set_ylabel("Accuracy")
+    axs[1].set_ylim(bottom=0, top=1)
+    axs[1].legend()
+    axs[1].grid(visible=True, which="both", linewidth=0.5, alpha=0.5)
+    axs[1].minorticks_on()
+    axs[1].set_xlabel("Rounds")
+
+    plt.tight_layout()
+    plt.savefig(Path(save_path) / Path("loss_accuracy.png"), dpi=300)
+    plt.close()
+
+
+def plot_histories_with_accuracy(axs, histories, label):
+    """Plot histories' accuracy and loss."""
+    accuracies_across_runs = []
+    losses_across_runs = []
+
+    for run in histories:
+        history_metrics = run.metrics_centralized["accuracy"]
+        history_losses = run.losses_centralized
+
+        rounds, accuracy_values = zip(*history_metrics)
+        _, loss_values = zip(*history_losses)
+        accuracies_across_runs.append(accuracy_values)
+        losses_across_runs.append(loss_values)
+
+    plot_accuracy_and_loss(
+        axs, accuracies_across_runs, losses_across_runs, rounds, label
+    )
+
+
+def plot_accuracy_and_loss(
+    axs, accuracies_across_runs, losses_across_runs, rounds, label
+):
+    """Plot histories' accuracy and loss."""
+    x_axis = list(rounds)
+    lowest_loss_across_runs = np.min(losses_across_runs, axis=0)
+    highest_loss_across_runs = np.max(losses_across_runs, axis=0)
+    lowest_accuracy_across_runs = np.min(accuracies_across_runs, axis=0)
+    highest_accuracy_across_runs = np.max(accuracies_across_runs, axis=0)
+    avg_loss_across_runs = np.add(lowest_loss_across_runs, highest_loss_across_runs) / 2
+    avg_accuracy_across_runs = (
+        np.add(lowest_accuracy_across_runs, highest_accuracy_across_runs) / 2
+    )
+    axs[0].fill_between(
+        x_axis,
+        highest_loss_across_runs,
+        lowest_loss_across_runs,
+        alpha=0.4,
+        label=label,
+    )
+    axs[0].plot(x_axis, avg_loss_across_runs, linewidth=2)
+    axs[1].fill_between(
+        x_axis,
+        highest_accuracy_across_runs,
+        lowest_accuracy_across_runs,
+        alpha=0.4,
+        label=label,
+    )
+    axs[1].plot(x_axis, avg_accuracy_across_runs, linewidth=2)

From f41563dd575ba23e44c3ba1fb1992fe7ef4d83c4 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Wed, 11 Oct 2023 10:05:06 +0200
Subject: [PATCH 55/60] Update quickstart-pandas requirements (#2479)

---
 examples/quickstart-pandas/pyproject.toml   | 10 +++++-----
 examples/quickstart-pandas/requirements.txt |  7 ++++---
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/examples/quickstart-pandas/pyproject.toml b/examples/quickstart-pandas/pyproject.toml
index b94c3c2e4f0e..de20eaf61d63 100644
--- a/examples/quickstart-pandas/pyproject.toml
+++ b/examples/quickstart-pandas/pyproject.toml
@@ -10,8 +10,8 @@ authors = ["Ragy Haddad <ragy202@gmail.com>"]
 maintainers = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
-python = "^3.8"
-flwr = "^1.0.0"
-numpy = "^1.21.0"
-pandas = "^2.0.0"
-scikit-learn = "^1.1.1"
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+numpy = "1.23.2"
+pandas = "2.0.0"
+scikit-learn = "1.3.1"
diff --git a/examples/quickstart-pandas/requirements.txt b/examples/quickstart-pandas/requirements.txt
index 994eed4fac0c..14308a55faaf 100644
--- a/examples/quickstart-pandas/requirements.txt
+++ b/examples/quickstart-pandas/requirements.txt
@@ -1,3 +1,4 @@
-flwr~=1.4.0
-numpy~=1.21.1
-pandas~=2.0.1
+flwr>=1.0, <2.0
+numpy==1.23.2
+pandas==2.0.0
+scikit-learn==1.3.1

From f777396cd5ec2bee3c29abb7c0888182b0b655db Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 11 Oct 2023 10:28:13 +0200
Subject: [PATCH 56/60] Sort tags by versions to get changelog (#2498)

---
 dev/get-latest-changelog.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/get-latest-changelog.sh b/dev/get-latest-changelog.sh
index d7f4ca7db168..8c03b257e309 100755
--- a/dev/get-latest-changelog.sh
+++ b/dev/get-latest-changelog.sh
@@ -5,7 +5,7 @@ cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 
 # Extract the latest release notes from the changelog, which starts at the line containing 
 # the latest version tag and ends one line before the previous version tag.
-tags=$(git tag --sort=-creatordate)
+tags=$(git tag --sort=-v:refname)
 new_version=$(echo "$tags" | sed -n '1p')
 old_version=$(echo "$tags" | sed -n '2p')
 

From 5aaf505a5a9ce0c76fbdac05613534f22b076d7d Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 11 Oct 2023 18:18:32 +0200
Subject: [PATCH 57/60] Add script to generate contributor log (#2452)

---
 dev/add-shortlog.sh         | 22 ++++++++++++++++++++++
 doc/source/ref-changelog.md |  2 +-
 2 files changed, 23 insertions(+), 1 deletion(-)
 create mode 100755 dev/add-shortlog.sh

diff --git a/dev/add-shortlog.sh b/dev/add-shortlog.sh
new file mode 100755
index 000000000000..2cd10f856aec
--- /dev/null
+++ b/dev/add-shortlog.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+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')
+
+shortlog=$(git shortlog "$old_version".."$new_version" -s | grep -vEi '(\(|\[)bot(\)|\])' | awk '{name = substr($0, index($0, $2)); printf "%s`%s`", sep, name; sep=", "} END {print ""}')
+
+token="<!---TOKEN_$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 $token"
+
+# Check if the token exists in the markdown file
+if ! grep -q "$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} $0 ~ "## " version {print text}' doc/source/ref-changelog.md > temp.md && mv temp.md doc/source/ref-changelog.md
+else
+    # If the token exists, replace the line containing the token with the new shortlog
+    awk -v token="$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
+fi
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 341c1d8dad77..f2390c2d7bf1 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -46,7 +46,7 @@
 
 We would like to give our special thanks to all the contributors who made the new version of Flower possible (in `git shortlog` order):
 
-`achiverram28`, `Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `eunchung`, `Gustavo Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Ruth Galindo`, `Steven Hé (Sīchàng)`, `Taner Topal`
+`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-->
 
 ### What's new?
 

From d1d456b115782a4b57553e832389954b2116b7ff Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 11 Oct 2023 18:23:04 +0200
Subject: [PATCH 58/60] Fix artifact URL on main (#2500)

---
 .github/workflows/e2e.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 238b9c0b862d..ab3b123d1042 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -41,7 +41,7 @@ jobs:
           echo "WHL_PATH=$(ls *.whl)" >> "$GITHUB_OUTPUT"
           sha_short=$(git rev-parse --short HEAD)
           echo "SHORT_SHA=$sha_short" >> "$GITHUB_OUTPUT"
-          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://artifact.flower.dev/py/${{ github.head_ref }}/$sha_short --recursive
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://artifact.flower.dev/py/${{ github.head_ref || github.ref_name }}/$sha_short --recursive
     outputs:
       whl_path: ${{ steps.upload.outputs.WHL_PATH }}
       short_sha: ${{ steps.upload.outputs.SHORT_SHA }}
@@ -123,7 +123,7 @@ jobs:
       - name: Install Flower wheel from artifact store
         if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
         run: |
-          python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
+          python -m pip install https://artifact.flower.dev/py/${{ github.head_ref || github.ref_name }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Download dataset
         if: ${{ matrix.dataset }}
         run: python -c "${{ matrix.dataset }}"
@@ -158,7 +158,7 @@ jobs:
       - name: Install Flower wheel from artifact store
         if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
         run: |
-          python -m pip install https://artifact.flower.dev/py/${{ github.head_ref }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
+          python -m pip install https://artifact.flower.dev/py/${{ github.head_ref || github.ref_name }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Cache Datasets
         uses: actions/cache@v3
         with:

From 67f03227f6f1a766e43edc11874069b8bead7d2e Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 11 Oct 2023 18:32:35 +0200
Subject: [PATCH 59/60] Add new auto release workflow (#2499)

---
 .github/workflows/framework-release.yml | 55 +++++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 .github/workflows/framework-release.yml

diff --git a/.github/workflows/framework-release.yml b/.github/workflows/framework-release.yml
new file mode 100644
index 000000000000..81f1ecf4263f
--- /dev/null
+++ b/.github/workflows/framework-release.yml
@@ -0,0 +1,55 @@
+name: Release Framework
+
+on:
+  push:
+    tags:
+      - "v*.*.*"
+
+jobs:
+  publish:
+    if: ${{ github.repository == 'adap/flower' }}
+    name: Publish draft
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Wait for wheel to be built
+        uses: lewagon/wait-on-check-action@v1.3.1
+        with:
+          ref: ${{ github.ref }}
+          check-name: 'Build, test and upload wheel'
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          wait-interval: 10
+      - name: Download wheel
+        run: |
+          tag_name=$(echo "${GITHUB_REF_NAME}" | cut -c2-)
+          echo "TAG_NAME=$tag_name" >> "$GITHUB_ENV"
+          
+          wheel_name="flwr-${tag_name}-py3-none-any.whl"
+          echo "WHEEL_NAME=$wheel_name" >> "$GITHUB_ENV"
+          
+          tar_name="flwr-${tag_name}.tar.gz"
+          echo "TAR_NAME=$tar_name" >> "$GITHUB_ENV"
+
+          wheel_url="https://artifact.flower.dev/py/main/${GITHUB_SHA::7}/${wheel_name}"
+          tar_url="https://artifact.flower.dev/py/main/${GITHUB_SHA::7}/${tar_name}"
+          
+          curl $wheel_url --output $wheel_name
+          curl $tar_url --output $tar_name
+
+      - name: Generate body
+        run: |
+          ./dev/get-latest-changelog.sh > body.md
+          cat body.md
+          
+      - name: Release
+        uses: softprops/action-gh-release@de2c0eb
+        with:
+          body_path: ./body.md
+          draft: true
+          name: Flower ${{ env.TAG_NAME }}
+          files: |
+            ${{ env.WHEEL_NAME }}
+            ${{ env.TAR_NAME }}

From 8f7488a48048c7579db81b00386dd6646c421d79 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Thu, 12 Oct 2023 09:59:26 +0200
Subject: [PATCH 60/60] Reorganize artifact folders (#2503)

---
 .github/workflows/e2e.yml               |  9 ++++++---
 .github/workflows/framework-release.yml | 10 +++++++++-
 2 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index ab3b123d1042..b868dc615399 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -41,10 +41,13 @@ jobs:
           echo "WHL_PATH=$(ls *.whl)" >> "$GITHUB_OUTPUT"
           sha_short=$(git rev-parse --short HEAD)
           echo "SHORT_SHA=$sha_short" >> "$GITHUB_OUTPUT"
-          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://artifact.flower.dev/py/${{ github.head_ref || github.ref_name }}/$sha_short --recursive
+          [ -z "${{ github.head_ref }}" ] && dir="${{ github.ref_name }}" || dir="pr/${{ github.head_ref }}"
+          echo "DIR=$dir" >> "$GITHUB_OUTPUT"
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://artifact.flower.dev/py/$dir/$sha_short --recursive
     outputs:
       whl_path: ${{ steps.upload.outputs.WHL_PATH }}
       short_sha: ${{ steps.upload.outputs.SHORT_SHA }}
+      dir: ${{ steps.upload.outputs.DIR }}
 
   frameworks:
     runs-on: ubuntu-22.04
@@ -123,7 +126,7 @@ jobs:
       - name: Install Flower wheel from artifact store
         if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
         run: |
-          python -m pip install https://artifact.flower.dev/py/${{ github.head_ref || github.ref_name }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
+          python -m pip install https://artifact.flower.dev/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Download dataset
         if: ${{ matrix.dataset }}
         run: python -c "${{ matrix.dataset }}"
@@ -158,7 +161,7 @@ jobs:
       - name: Install Flower wheel from artifact store
         if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
         run: |
-          python -m pip install https://artifact.flower.dev/py/${{ github.head_ref || github.ref_name }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
+          python -m pip install https://artifact.flower.dev/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Cache Datasets
         uses: actions/cache@v3
         with:
diff --git a/.github/workflows/framework-release.yml b/.github/workflows/framework-release.yml
index 81f1ecf4263f..2d2b7d8a4c4f 100644
--- a/.github/workflows/framework-release.yml
+++ b/.github/workflows/framework-release.yml
@@ -38,7 +38,15 @@ jobs:
           
           curl $wheel_url --output $wheel_name
           curl $tar_url --output $tar_name
-
+      - name: Upload wheel
+        env:
+          AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
+          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
+        run: |
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.WHEEL_NAME }} s3://artifact.flower.dev/py/release/v${{ env.TAG_NAME }}/${{ env.WHEEL_NAME }}
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.TAR_NAME }} s3://artifact.flower.dev/py/release/v${{ env.TAG_NAME }}/${{ env.TAR_NAME }}
+          
       - name: Generate body
         run: |
           ./dev/get-latest-changelog.sh > body.md