Federated Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

FedSSA, a novel personalized heterogeneous federated learning framework, enhances the performance and efficiency of model-heterogeneous personalized federated learning through semantic similarity-based header parameter aggregation and adaptive parameter stabilization.