diff --git a/examples/xgboost-quickstart/README.md b/examples/xgboost-quickstart/README.md
new file mode 100644
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+++ b/examples/xgboost-quickstart/README.md
@@ -0,0 +1,85 @@
+# 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
+-- 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
+```
+
+Start client 2 in the second terminal:
+
+```shell
+python3 client.py
+```
+
+You will see that XGBoost is starting a federated training.
+
+Alternatively, you can use `run.sh` to run the same experiment in a single terminal as follows:
+
+```shell
+bash run.sh
+```
+
+Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-xgboost)
+and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
diff --git a/examples/xgboost-quickstart/client.py b/examples/xgboost-quickstart/client.py
new file mode 100644
index 000000000000..f6c6f2e34722
--- /dev/null
+++ b/examples/xgboost-quickstart/client.py
@@ -0,0 +1,144 @@
+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 flwr_datasets.partitioner import IidPartitioner
+
+from dataset import (
+    train_test_split,
+    transform_dataset_to_dmatrix,
+)
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+# Load (HIGGS) dataset and conduct partitioning
+partitioner = IidPartitioner(num_partitions=10)
+fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})
+
+# Let's use the first partition as an example
+partition = fds.load_partition(idx=0, split="train")
+partition.set_format("numpy")
+
+# Train/test splitting
+train_data, valid_data, num_train, num_val = train_test_split(
+    partition, test_fraction=0.2, seed=42
+)
+
+# Reformat data to DMatrix for xgboost
+train_dmatrix = transform_dataset_to_dmatrix(train_data)
+valid_dmatrix = transform_dataset_to_dmatrix(valid_data)
+
+# Hyper-parameters for xgboost training
+num_local_round = 1
+params = {
+    "objective": "binary:logistic",
+    "eta": 0.1,  # Learning rate
+    "max_depth": 8,
+    "eval_metric": "auc",
+    "nthread": 16,
+    "num_parallel_tree": 1,
+    "subsample": 1,
+    "tree_method": "hist",
+}
+
+
+# Define Flower client
+class FlowerClient(fl.client.Client):
+    def __init__(self):
+        self.bst = None
+        self.config = None
+
+    def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
+        _ = (self, ins)
+        return GetParametersRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[]),
+        )
+
+    def _local_boost(self):
+        # Update trees based on local training data.
+        for i in range(num_local_round):
+            self.bst.update(train_dmatrix, self.bst.num_boosted_rounds())
+
+        # Extract the last N=num_local_round trees for sever aggregation
+        bst = self.bst[
+            self.bst.num_boosted_rounds()
+            - num_local_round : self.bst.num_boosted_rounds()
+        ]
+
+        return bst
+
+    def fit(self, ins: FitIns) -> FitRes:
+        if not self.bst:
+            # First round local training
+            log(INFO, "Start training at round 1")
+            bst = xgb.train(
+                params,
+                train_dmatrix,
+                num_boost_round=num_local_round,
+                evals=[(valid_dmatrix, "validate"), (train_dmatrix, "train")],
+            )
+            self.config = bst.save_config()
+            self.bst = bst
+        else:
+            for item in ins.parameters.tensors:
+                global_model = bytearray(item)
+
+            # Load global model into booster
+            self.bst.load_model(global_model)
+            self.bst.load_config(self.config)
+
+            bst = self._local_boost()
+
+        local_model = bst.save_raw("json")
+        local_model_bytes = bytes(local_model)
+
+        return FitRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[local_model_bytes]),
+            num_examples=num_train,
+            metrics={},
+        )
+
+    def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
+        eval_results = self.bst.eval_set(
+            evals=[(valid_dmatrix, "valid")],
+            iteration=self.bst.num_boosted_rounds() - 1,
+        )
+        auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4)
+
+        return EvaluateRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            loss=0.0,
+            num_examples=num_val,
+            metrics={"AUC": auc},
+        )
+
+
+# Start Flower client
+fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient())
diff --git a/examples/xgboost-quickstart/dataset.py b/examples/xgboost-quickstart/dataset.py
new file mode 100644
index 000000000000..95455618937b
--- /dev/null
+++ b/examples/xgboost-quickstart/dataset.py
@@ -0,0 +1,23 @@
+import xgboost as xgb
+from typing import Union
+from datasets import Dataset, DatasetDict
+
+
+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
diff --git a/examples/xgboost-quickstart/pyproject.toml b/examples/xgboost-quickstart/pyproject.toml
new file mode 100644
index 000000000000..74256846c693
--- /dev/null
+++ b/examples/xgboost-quickstart/pyproject.toml
@@ -0,0 +1,15 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "xgboost-quickstart"
+version = "0.1.0"
+description = "Federated XGBoost with Flower (quickstart)"
+authors = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+flwr-datasets = ">=0.0.1,<1.0.0"
+xgboost = ">=2.0.0,<3.0.0"
diff --git a/examples/xgboost-quickstart/requirements.txt b/examples/xgboost-quickstart/requirements.txt
new file mode 100644
index 000000000000..9596a8d6cd02
--- /dev/null
+++ b/examples/xgboost-quickstart/requirements.txt
@@ -0,0 +1,3 @@
+flwr>=1.0, <2.0
+flwr-datasets>=0.0.1, <1.0.0
+xgboost>=2.0.0, <3.0.0
diff --git a/examples/xgboost-quickstart/run.sh b/examples/xgboost-quickstart/run.sh
new file mode 100755
index 000000000000..b3ab546022d8
--- /dev/null
+++ b/examples/xgboost-quickstart/run.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
+
+echo "Starting server"
+python server.py &
+sleep 5  # Sleep for 15s to give the server enough time to start
+
+for i in `seq 0 1`; do
+    echo "Starting client $i"
+    python3 client.py &
+done
+
+# Enable CTRL+C to stop all background processes
+trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+# Wait for all background processes to complete
+wait
diff --git a/examples/xgboost-quickstart/server.py b/examples/xgboost-quickstart/server.py
new file mode 100644
index 000000000000..d287eae596e7
--- /dev/null
+++ b/examples/xgboost-quickstart/server.py
@@ -0,0 +1,37 @@
+import flwr as fl
+from strategy import FedXgbBagging
+
+
+# FL experimental settings
+pool_size = 2
+num_rounds = 5
+num_clients_per_round = 2
+num_evaluate_clients = 2
+
+
+def evaluate_metrics_aggregation(eval_metrics):
+    """Return an aggregated metric (AUC) for evaluation."""
+    total_num = sum([num for num, _ in eval_metrics])
+    auc_aggregated = (
+        sum([metrics["AUC"] * num for num, metrics in eval_metrics]) / total_num
+    )
+    metrics_aggregated = {"AUC": auc_aggregated}
+    return metrics_aggregated
+
+
+# Define strategy
+strategy = FedXgbBagging(
+    fraction_fit=(float(num_clients_per_round) / pool_size),
+    min_fit_clients=num_clients_per_round,
+    min_available_clients=pool_size,
+    min_evaluate_clients=num_evaluate_clients,
+    fraction_evaluate=1.0,
+    evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation,
+)
+
+# Start Flower server
+fl.server.start_server(
+    server_address="0.0.0.0:8080",
+    config=fl.server.ServerConfig(num_rounds=num_rounds),
+    strategy=strategy,
+)
diff --git a/examples/xgboost-quickstart/strategy.py b/examples/xgboost-quickstart/strategy.py
new file mode 100644
index 000000000000..578328ca701b
--- /dev/null
+++ b/examples/xgboost-quickstart/strategy.py
@@ -0,0 +1,163 @@
+# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Federated XGBoost bagging aggregation strategy."""
+
+
+import json
+from logging import WARNING
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
+
+import flwr as fl
+from flwr.common import EvaluateRes, FitRes, Parameters, Scalar
+from flwr.common.logger import log
+from flwr.server.client_proxy import ClientProxy
+
+
+class FedXgbBagging(fl.server.strategy.FedAvg):
+    """Configurable FedXgbBagging strategy implementation."""
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long
+    def __init__(
+        self,
+        evaluate_function: Optional[
+            Callable[
+                [int, Parameters, Dict[str, Scalar]],
+                Optional[Tuple[float, Dict[str, Scalar]]],
+            ]
+        ] = None,
+        **kwargs: Any,
+    ):
+        self.evaluate_function = evaluate_function
+        self.global_model: Optional[bytes] = None
+        super().__init__(**kwargs)
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate fit results using bagging."""
+        if not results:
+            return None, {}
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Aggregate all the client trees
+        global_model = self.global_model
+        for _, fit_res in results:
+            update = fit_res.parameters.tensors
+            for bst in update:
+                global_model = aggregate(global_model, bst)
+
+        self.global_model = global_model
+
+        return (
+            Parameters(tensor_type="", tensors=[cast(bytes, global_model)]),
+            {},
+        )
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation metrics using average."""
+        if not results:
+            return None, {}
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Aggregate custom metrics if aggregation fn was provided
+        metrics_aggregated = {}
+        if self.evaluate_metrics_aggregation_fn:
+            eval_metrics = [(res.num_examples, res.metrics) for _, res in results]
+            metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics)
+        elif server_round == 1:  # Only log this warning once
+            log(WARNING, "No evaluate_metrics_aggregation_fn provided")
+
+        return 0, metrics_aggregated
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function."""
+        if self.evaluate_function is None:
+            # No evaluation function provided
+            return None
+        eval_res = self.evaluate_function(server_round, parameters, {})
+        if eval_res is None:
+            return None
+        loss, metrics = eval_res
+        return loss, metrics
+
+
+def aggregate(
+    bst_prev_org: Optional[bytes],
+    bst_curr_org: bytes,
+) -> bytes:
+    """Conduct bagging aggregation for given trees."""
+    if not bst_prev_org:
+        return bst_curr_org
+
+    # Get the tree numbers
+    tree_num_prev, _ = _get_tree_nums(bst_prev_org)
+    _, paral_tree_num_curr = _get_tree_nums(bst_curr_org)
+
+    bst_prev = json.loads(bytearray(bst_prev_org))
+    bst_curr = json.loads(bytearray(bst_curr_org))
+
+    bst_prev["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+        "num_trees"
+    ] = str(tree_num_prev + paral_tree_num_curr)
+    iteration_indptr = bst_prev["learner"]["gradient_booster"]["model"][
+        "iteration_indptr"
+    ]
+    bst_prev["learner"]["gradient_booster"]["model"]["iteration_indptr"].append(
+        iteration_indptr[-1] + paral_tree_num_curr
+    )
+
+    # Aggregate new trees
+    trees_curr = bst_curr["learner"]["gradient_booster"]["model"]["trees"]
+    for tree_count in range(paral_tree_num_curr):
+        trees_curr[tree_count]["id"] = tree_num_prev + tree_count
+        bst_prev["learner"]["gradient_booster"]["model"]["trees"].append(
+            trees_curr[tree_count]
+        )
+        bst_prev["learner"]["gradient_booster"]["model"]["tree_info"].append(0)
+
+    bst_prev_bytes = bytes(json.dumps(bst_prev), "utf-8")
+
+    return bst_prev_bytes
+
+
+def _get_tree_nums(xgb_model_org: bytes) -> Tuple[int, int]:
+    xgb_model = json.loads(bytearray(xgb_model_org))
+    # Get the number of trees
+    tree_num = int(
+        xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+            "num_trees"
+        ]
+    )
+    # Get the number of parallel trees
+    paral_tree_num = int(
+        xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"][
+            "num_parallel_tree"
+        ]
+    )
+    return tree_num, paral_tree_num