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