personalized-fl

This paper introduces COSMOS, a model-agnostic personalized federated learning framework that uses clustered server models and pseudo-label-only communication. It provides theoretical analysis showing exponential personalization risk contraction and demonstrates superior performance over existing baselines in heterogeneous environments.