Recently, deep learning has been successfully applied in numerous domains and is increasingly being integrated into healthcare and clinical research. The ability to combine diverse data sources such as genomics and imaging enhances medical decision-making. Access to large and heterogeneous datasets is essential for improving model quality and predictive accuracy. Federated learning is currently developed to support this requirement offering an alternative by enabling decentralized model training while ensuring that raw data remains stored locally at the client side. Several open-source frameworks integrate secure computation protocols for federated learning but remains limited in its applicability to healthcare and raises issues related to data sovereignty. In this context, a French framework is currently developed by the CEA-LIST, introduces an edge-to-cloud federated learning architecture that incorporates end-to-end encryption, including fully homomorphic encryption (FHE) and resilience against adversarial threats. Through this framework, this project aims to deliver modular and secure federated learning components that foster further innovation in healthcare AI.
This project will focus on three core themes:
1) Deployment, monitoring and optimization of deep learning models within federated and decentralized learning solutions.
2) Integrating large models in collaborative learning.
3) Developing aggregation methods for non-IID situation.