Pybiscus is a simple tool to perform Federated Learning on various models and datasets. It aims at automated as much as possible the FL pipeline, and allows to add virtually any kind of dataset and model.

Pybiscus is built on top of Flower, a mature Federated Learning framework; jsonargparse (script and CLI parts) and Lightning/Fabric for all the Machine Learning machinery.

The project is still a work-in-progress, and Pybiscus is not yet available as a proper Python package. To use Pybiscus, the best course of action for now is to use Rye, a very powerful comprehensive project and package management solution for Python.

Using Rye, it is as simple as

rye sync --all-features

if you aim at contributing to the project, or simply

rye sync

to install only the core dependencies.

Documentation is available at docs.

If you are interested in contributing to the Pybiscus project, start by reading the Contributing guide.

Pybiscus is on active development at Thales, both for internal use and on some collaborative projects. One major use is in the Europeean Project PAROMA-MED, dedicated to Federated Learning in the context of medical data distributed among several Hospitals.

The License is Apache 2.0. You can find all the relevant information here LICENSE