Our Goal

Fed-BioMed is an open-source research and development initiative for translating collaborative learning into real-world medical applications, including federated learning and federated analytics.
The community of Fed-BioMed gathers experts in medical engineering, machine learning, communication, and security.
We all contribute to provide an open, user-friendly, and trusted framework for deploying the state-of-the-art of collaborative learning in sensitive environments, such as in hospitals and health data lakes.

Easy Deployment
Easily deploy state-of-the art collaborative learning analysis frameworks
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)
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.
Software Installation
Learn how to install Fed-BioMed modules on your machine and start Fed-BioMed tutorials.
Follow our tutorials to know more about Fed-BioMed
MONAI Scikit-Learn PyTorch
User Guide
Learn details about Fed-BioMed framework.

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 faithfully represents the global variability across hospitals, and provides high generalization and robustness properties.


Fed-BioMed @ WAICF 2024

Fed-BioMed at WAICF 2024 on February 8-9

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A new release of Fed-BioMed (v5.0) is out!

A new release of Fed-BioMed is now available, introducing gRPC communication layer !

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Fed-BioMed @ Viva Technology 2023

Fed-BioMed at Viva Technology 2023 on June 16

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Industrial Contributors

Users and Partners

User support

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

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