Federated Learning with scikit-learn and Flower (Quickstart Example)#
This example of Flower uses scikit-learn
βs LogisticRegression
model to train a federated learning system on
βirisβ (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
βββ sklearnexample
β βββ __init__.py
β βββ client_app.py # Defines your ClientApp
β βββ server_app.py # Defines your ServerApp
β βββ task.py # Defines your model, training and data loading
βββ pyproject.toml # Project metadata like dependencies and configs
βββ 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.