Flower Example using scikit-learn

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.

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:

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 containing the following files:

-- pyproject.toml
-- requirements.txt
-- client.py
-- server.py
-- utils.py
-- README.md

Installing Dependencies

Project dependencies (such as scikit-learn and flwr) are defined in pyproject.toml and requirements.txt. We recommend Poetry to install those dependencies and manage your virtual environment (Poetry installation) or pip, but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.

Poetry

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:

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.

pip install -r requirements.txt

Run Federated Learning with scikit-learn 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:

poetry run 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 terminals and run the following command in each:

poetry run python3 client.py --partition-id 0 # partition-id should be any of {0,1,2}

Alternatively you can run all of it in one shell as follows:

poetry run python3 server.py &
poetry run python3 client.py --partition-id 0 &
poetry run python3 client.py --partition-id 1

You will see that Flower is starting a federated training.