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; Typer (script and CLI parts) and Lightning/Fabric for all the Machine Learning machinery.
You can simply test Pybiscus by downloading the latest wheel available and install it.
You can simply test Pybiscus by downloading the latest wheel available in the dist folder and install it in a virtual environnement:
python -m pip install virtualenv
python -m virtualenv .venv
source .venv/bin/activate
(.venv) python -m pip install pybiscus_paroma-0.5.0-py3-none-any.whland you are good to go! The packages comes with an app named pybiscus_paroma_app that you can use in the virtual environment. You can then test if everything went well by launching a local training:
(.venv) pybiscus_paroma_app local train-config configs/local_train.ymlDocumentation 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