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 .

[!NOTE] For MacOSX users, you may need to additionally run brew install libomp to install OpenMP runtime.

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

[!NOTE] Check the Simulation Engine documentation to learn more about Flower simulations and how to optimize them.

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

Follow this how-to guide to run the same app in this example but with Flower’s Deployment Engine. After that, you might be intersted in setting up secure TLS-enabled communications and SuperNode authentication in your federation.

If you are already familiar with how the Deployment Engine works, you may want to learn how to run it using Docker. Check out the Flower with Docker documentation.