About This Architecture

FedReMa is a federated learning architecture enabling hospitals to train personalized models collaboratively without sharing raw patient data. Each hospital runs GEANet Feature Extraction locally, sending only learned features to a centralized FedReMa Cloud Server with Aggregation and Personalization modules. The server aggregates features across Hospital A, B, and C, personalizes models via the Personalization Module, and distributes customized models back to each site while maintaining a Model Registry and Parameter Storage. This approach solves the critical healthcare challenge of improving model accuracy across diverse patient populations while preserving data privacy and regulatory compliance. Fork this diagram on Diagrams.so to customize hospital nodes, add new federation members, or integrate with your privacy-preserving ML pipeline. The bidirectional flow between personalized models and feature extraction demonstrates how federated learning enables continuous improvement without centralizing sensitive medical data.

People also ask

How can hospitals train machine learning models together without sharing patient data?

FedReMa implements federated learning where each hospital extracts features locally using GEANet, sends only learned representations to a centralized Personalization Module, and receives customized models back. This preserves patient privacy while enabling collaborative model improvement across Hospital A, B, and C through centralized aggregation and personalization.