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.