Federated Learning with XGBoost and Flower (Quickstart Example)¶

This example demonstrates how to perform EXtreme Gradient Boosting (XGBoost) within Flower using xgboost package. We use HIGGS dataset for this example to perform a binary classification task. Tree-based with bagging method is used for aggregation on the server.

This project provides a minimal code example to enable you to get started quickly. For a more comprehensive code example, take a look at xgboost-comprehensive.

Set up the project¶

Clone the project¶

Start by cloning the example project:

git clone --depth=1 https://github.com/adap/flower.git _tmp \
        && mv _tmp/examples/xgboost-quickstart . \
        && rm -rf _tmp \
        && cd xgboost-quickstart

This will create a new directory called xgboost-quickstart with the following structure:

xgboost-quickstart
├── xgboost_quickstart
│   ├── __init__.py
│   ├── client_app.py   # Defines your ClientApp
│   ├── server_app.py   # Defines your ServerApp
│   └── task.py         # Defines your utilities and data loading
├── pyproject.toml      # Project metadata like dependencies and configs
└── README.md

Install dependencies and project¶

Install the dependencies defined in pyproject.toml as well as the xgboost_quickstart package.

pip install -e .

Run the project¶

You can run your Flower project in both simulation and deployment mode without making changes to the code. If you are starting with Flower, we recommend you using the simulation mode as it requires fewer components to be launched manually. By default, flwr run will make use of the Simulation Engine.

Run with the Simulation Engine¶

flwr run .

You can also override some of the settings for your ClientApp and ServerApp defined in pyproject.toml. For example:

flwr run . --run-config "num-server-rounds=5 params.eta=0.05"

[!TIP] For a more detailed walk-through check our quickstart XGBoost tutorial

Run with the Deployment Engine¶

[!NOTE] An update to this example will show how to run this Flower application with the Deployment Engine and TLS certificates, or with Docker.