Communication-Efficient Personalized Federated Learning via Layer-Wise Multi-Threshold Random Sketching

Personalized federated learning (PFL) is a promising paradigm for collaborative learning over distributed devices, where edge nodes collaboratively train personalized models without sharing raw data. Although PFL addresses data heterogeneity by learning client-specific models, it still suffers from