Our Goal

Fed-BioMed is an open-source research and development initiative for translating federated learning into real-world medical applications.
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 federated learning in sensitive environments, such as in hospitals and health data lakes.


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 (v4.3) is out!

A new release of Fed-BioMed is now available, introducing Secure Aggregation !

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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

Read More

Fed-BioMed for Federated-PET project

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

Read More

Funding


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