xgboost-comprehensive with bagging aggregation

examples/quickstart-xgboost/README.md

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+# Flower Example using XGBoost
+
+This example demonstrates how to perform EXtreme Gradient Boosting (XGBoost) within Flower using `xgboost` package.
+Tree-based with bagging method is used for aggregation on the server.
+
+## Project Setup
+
+Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you:
+
+```shell
+git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/quickstart-xgboost . && rm -rf flower && cd quickstart-xgboost
+```
+
+This will create a new directory called `quickstart-xgboost` containing the following files:
+
+```
+-- README.md         <- Your're reading this right now
+-- server.py         <- Defines the server-side logic
+-- strategy.py       <- Defines the tree-based bagging aggregation
+-- client.py         <- Defines the client-side logic
+-- dataset.py        <- Defines the functions of data loading and partitioning
+-- pyproject.toml    <- Example dependencies (if you use Poetry)
+-- requirements.txt  <- Example dependencies
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `xgboost` and `flwr`) are defined in `pyproject.toml` and `requirements.txt`. We recommend [Poetry](https://python-poetry.org/docs/) to install those dependencies and manage your virtual environment ([Poetry installation](https://python-poetry.org/docs/#installation)) or [pip](https://pip.pypa.io/en/latest/development/), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.
+
+#### Poetry
+
+```shell
+poetry install
+poetry shell
+```
+
+Poetry will install all your dependencies in a newly created virtual environment. To verify that everything works correctly you can run the following command:
+
+```shell
+poetry run python3 -c "import flwr"
+```
+
+If you don't see any errors you're good to go!
+
+#### pip
+
+Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt.
+
+```shell
+pip install -r requirements.txt
+```
+
+## Run Federated Learning with XGBoost and Flower
+
+Afterwards you are ready to start the Flower server as well as the clients. 
+You can simply start the server in a terminal as follows:
+
+```shell
+python3 server.py
+```
+
+Now you are ready to start the Flower clients which will participate in the learning. 
+To do so simply open two more terminal windows and run the following commands.
+
+Start client 1 in the first terminal:
+
+```shell
+python3 client.py partition_id=0
+```
+
+Start client 2 in the second terminal:
+
+```shell
+python3 client.py partition_id=1
+```
+
+You will see that XGBoost is starting a federated training.
+
+Alternatively, you can use `run.sh` to run the same experiment in a single terminal as follows:
+
+```shell
+bash run.sh
+```
+
+Besides, we provide options to customise the experimental settings, including data partitioning and centralised/distributed evaluation (see `utils.py`).
+Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-xgboost) 
+and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.

examples/quickstart-xgboost/client.py

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+import warnings
+from logging import INFO
+import xgboost as xgb
+
+import flwr as fl
+from flwr_datasets import FederatedDataset
+from flwr.common.logger import log
+from flwr.common import (
+    Code,
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    GetParametersIns,
+    GetParametersRes,
+    Parameters,
+    Status,
+)
+
+from dataset import (
+    instantiate_partitioner,
+    train_test_split,
+    transform_dataset_to_dmatrix,
+    resplit,
+)
+from utils import client_args_parser
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+# Parse arguments for experimental settings
+args = client_args_parser()
+
+# Load (HIGGS) dataset and conduct partitioning
+num_partitions = args.num_partitions
+# Partitioner type is chosen from ["uniform", "linear", "square", "exponential"]
+partitioner_type = args.partitioner_type
+
+# Instantiate partitioner
+partitioner = instantiate_partitioner(
+    partitioner_type=partitioner_type, num_partitions=num_partitions
+)
+fds = FederatedDataset(
+    dataset="jxie/higgs", partitioners={"train": partitioner}, resplitter=resplit
+)
+
+# Let's use the first partition as an example
+partition_id = args.partition_id
+partition = fds.load_partition(idx=partition_id, split="train")
+partition.set_format("numpy")
+
+if args.centralised_eval:
+    # Use centralised test set for evaluation
+    train_data = partition
+    valid_data = fds.load_full("test")
+    valid_data.set_format("numpy")
+    num_train = train_data.shape[0]
+    num_val = valid_data.shape[0]
+else:
+    # Train/test splitting
+    SEED = args.seed
+    test_fraction = args.test_fraction
+    train_data, valid_data, num_train, num_val = train_test_split(
+        partition, test_fraction=test_fraction, seed=SEED
+    )
+
+# Reformat data to DMatrix for xgboost
+train_dmatrix = transform_dataset_to_dmatrix(train_data)
+valid_dmatrix = transform_dataset_to_dmatrix(valid_data)
+
+
+# Hyper-parameters for xgboost training
+num_local_round = 1
+params = {
+    "objective": "binary:logistic",
+    "eta": 0.1,  # Learning rate
+    "max_depth": 8,
+    "eval_metric": "auc",
+    "nthread": 16,
+    "num_parallel_tree": 1,
+    "subsample": 1,
+    "tree_method": "hist",
+}
+
+
+# Define Flower client
+class FlowerClient(fl.client.Client):
+    def __init__(self):
+        self.bst = None
+        self.config = None
+
+    def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
+        _ = (self, ins)
+        return GetParametersRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[]),
+        )
+
+    def _local_boost(self):
+        # Update trees based on local training data.
+        for i in range(num_local_round):
+            self.bst.update(train_dmatrix, self.bst.num_boosted_rounds())
+
+        # Extract the last N=num_local_round trees for sever aggregation
+        bst = self.bst[
+            self.bst.num_boosted_rounds()
+            - num_local_round : self.bst.num_boosted_rounds()
+        ]
+
+        return bst
+
+    def fit(self, ins: FitIns) -> FitRes:
+        if not self.bst:
+            # First round local training
+            log(INFO, "Start training at round 1")
+            bst = xgb.train(
+                params,
+                train_dmatrix,
+                num_boost_round=num_local_round,
+                evals=[(valid_dmatrix, "validate"), (train_dmatrix, "train")],
+            )
+            self.config = bst.save_config()
+            self.bst = bst
+        else:
+            for item in ins.parameters.tensors:
+                global_model = bytearray(item)
+
+            # Load global model into booster
+            self.bst.load_model(global_model)
+            self.bst.load_config(self.config)
+
+            bst = self._local_boost()
+
+        local_model = bst.save_raw("json")
+        local_model_bytes = bytes(local_model)
+
+        return FitRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[local_model_bytes]),
+            num_examples=num_train,
+            metrics={},
+        )
+
+    def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
+        eval_results = self.bst.eval_set(
+            evals=[(valid_dmatrix, "valid")],
+            iteration=self.bst.num_boosted_rounds() - 1,
+        )
+        auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4)
+
+        global_round = ins.config["global_round"]
+        log(INFO, f"AUC = {auc} at round {global_round}")
+
+        return EvaluateRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            loss=0.0,
+            num_examples=num_val,
+            metrics={"AUC": auc},
+        )
+
+
+# Start Flower client
+fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient())

examples/quickstart-xgboost/dataset.py

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+import xgboost as xgb
+from typing import Callable, Dict, List, Optional, Tuple, Union
+from datasets import Dataset, DatasetDict, concatenate_datasets
+from flwr_datasets.partitioner import (
+    IidPartitioner,
+    LinearPartitioner,
+    SquarePartitioner,
+    ExponentialPartitioner,
+)
+
+CORRELATION_TO_PARTITIONER = {
+    "uniform": IidPartitioner,
+    "linear": LinearPartitioner,
+    "square": SquarePartitioner,
+    "exponential": ExponentialPartitioner,
+}
+
+
+def instantiate_partitioner(partitioner_type: str, num_partitions: int):
+    """Initialise partitioner based on selected partitioner type and number of
+    partitions."""
+    partitioner = CORRELATION_TO_PARTITIONER[partitioner_type](
+        num_partitions=num_partitions
+    )
+    return partitioner
+
+
+def train_test_split(partition: Dataset, test_fraction: float, seed: int):
+    """Split the data into train and validation set given split rate."""
+    train_test = partition.train_test_split(test_size=test_fraction, seed=seed)
+    partition_train = train_test["train"]
+    partition_test = train_test["test"]
+
+    num_train = len(partition_train)
+    num_test = len(partition_test)
+
+    return partition_train, partition_test, num_train, num_test
+
+
+def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.DMatrix:
+    """Transform dataset to DMatrix format for xgboost."""
+    x = data["inputs"]
+    y = data["label"]
+    new_data = xgb.DMatrix(x, label=y)
+    return new_data
+
+
+def resplit(dataset: DatasetDict) -> DatasetDict:
+    """Increase the quantity of centralised test samples from 500K to 1M."""
+    return DatasetDict(
+        {
+            "train": dataset["train"].select(
+                range(0, dataset["train"].num_rows - 500_000)
+            ),
+            "test": concatenate_datasets(
+                [
+                    dataset["train"].select(
+                        range(
+                            dataset["train"].num_rows - 500_000,
+                            dataset["train"].num_rows,
+                        )
+                    ),
+                    dataset["test"],
+                ]
+            ),
+        }
+    )

examples/quickstart-xgboost/pyproject.toml

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+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "quickstart-xgboost"
+version = "0.1.0"
+description = "Federated XGBoost Quickstart with Flower"
+authors = ["The Flower Authors <[email protected]>"]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+flwr-datasets = ">=0.0.2,<1.0.0"
+xgboost = ">=2.0.0,<3.0.0"

examples/quickstart-xgboost/requirements.txt

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+flwr>=1.0, <2.0
+flwr-datasets>=0.0.2, <1.0.0
+xgboost>=2.0.0, <3.0.0

examples/quickstart-xgboost/run.sh

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+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
+
+echo "Starting server"
+python server.py &
+sleep 15  # Sleep for 15s to give the server enough time to start
+
+for i in `seq 0 1`; do
+    echo "Starting client $i"
+    python client.py --partition_id=$i &
+done
+
+# Enable CTRL+C to stop all background processes
+trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+# Wait for all background processes to complete
+wait