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.
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.
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