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Fed-BioMed is a research and development initiative aiming at translating federated learning (FL) to healthcare applications. FL has appealing guarantees about the respect of the data governance, since it allows us to train AI models across different hospitals by sharing model parameters only, instead of data.
However, the translation of FL research into practice poses novel technical and societal questions, and the deployment of FL requires to tackle important challenges to meet the strict requirements of real-world conditions. Typical problems to be addressed concern FL security, scalability and interoperability, which motivate novel research directions and promote the close interactions between researchers, technicians and healthcare practitioners.
During the talk I will provide an illustration of the translational effort that characterise the development of the Fed-BioMed FL platform, and discuss our current effort in delivering FL across health institutions in France.
Keywords: federated learning, multi-centric studies, artificial intelligence, data security, data governance