Our Goal

Fed-BioMed is an open source project focused on empowering biomedical research using non-centralized approaches for statistical analysis and machine learning. The project is currently based on Python, PyTorch and Scikit-learn, and enables developing and deploying federated learning analysis in real-world machine learning applications.


Easy Deployment
Easily deploy state-of-the art federated learning analysis frameworks
Security
Strong focus on security in communications and machine learning
Model Deployment
Multiframework support to easily deploy models and analysis methods (PyTorch, Scikit-Learn, MONAI, numpy)
Collaboration
Foster research and collaborations in federated learning.

Let's Start

Start using Fed-BioMed, follow our tutorials and user guide.

Getting Started
Learn about Fed-BioMed framework and see how we convert local training to federated training.
Start
Software Installation
Learn how to install Fed-BioMed modules on your machine and start Fed-BioMed tutorials.
Installation
Tutorials
Follow our tutorials to know more about Fed-BioMed
MONAI Scikit-Learn PyTorch
User Guide
Learn details about Fed-BioMed framework.
Docs

What's federated learning?

Discover the advantages of federated learning with Fed-BioMed

The goal of Federated learning is to allow collaborative learning with decentralized data.

Healthcare is a typical application of federated learning: while hospitals across several geographical locations want to jointly train a machine learning model on the data hosted at each site, data cannot be shared between them because of privacy and security concerns. Federated learning gives us a methodological framework to train a global machine learning model, by only sharing the parameters of the models separately trained at each site. As a result, data never leaves the hospitals, and training is performed by simply aggregating models parameters to finally obtain a global model. Under certain conditions, the aggregated model faitfully represents the global variability across hospitals, and provides high generalization and robustness properties.


News

A new release of Fed-BioMed (v4.1) is out!

A new release of Fed-BioMed (v4.1) is out, introducing Scaffold aggregator, more integration tests

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Fed-BioMed for Federated-PET project

Federated-PET groups 8 hospitals and 4 research centers in an oncology research project.

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Fed-BioMed @ Open Source Experience 2022

Fed-BioMed participated to *Open Source Experience 2022* meeting of the European open source community, Paris, Nov 8-9.

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Funding


Industrial Contributors


Users and Partners


User support

Send a message to fedbiomed-support _at_ inria _dot_ fr or on the the Fed-BioMed support channel on Discord
and benefit from the feedback of the community.

Please begin with checking the support list archive and issues archive for an existing answer to your problem.

When posting a support request, please pay attention to some tips:

  • Be clear about what your problem is: what was the expected outcome,
    what happened instead? Detail how someone else can recreate the problem.
  • Additional infos: link to demos, screenshots or code showing the problem.

You may also want to subscribe to the support list.

Contact Us

If you want to be part of Fed-BioMed contact fedbiomed _at_ inria _dot_ fr