Federated Learning with scikit-learn and Flower (Quickstart Example)

This example of Flower uses scikit-learn’s LogisticRegression model to train a federated learning system on “iris” (tabular) dataset. It will help you understand how to adapt Flower for use with scikit-learn. Running this example in itself is quite easy. This example uses Flower Datasets to download, partition and preprocess the dataset.

Set up the project

Clone the project

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:

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

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

quickstart-sklearn-tabular
├── sklearnexample
│   ├── __init__.py
│   ├── client_app.py   # Defines your ClientApp
│   ├── server_app.py   # Defines your ServerApp
│   └── task.py         # Defines your model, training 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 sklearnexample 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 penalty="'l1'"

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.