Kaili Jin

FedCPD:Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation

Federated learning, as a distributed learning framework, aims to develop a global model while preserving client privacy. However, heterogeneity of client data leads to fairness issues and reduced performance. Techniques like parameter decoupling and prototype learning appear promising, yet challenge