Tackling Data Heterogeneity in Federated Learning with Class Prototypes
Tackling Data Heterogeneity in Federated Learning with Class Prototypes
Data heterogeneity across clients in federated learning (FL) settings is a widely acknowledged challenge. In response, personalized federated learning (PFL) emerged as a framework to curate local models for clients' tasks. In PFL, a common strategy is to develop local and global models jointly - the global model (for generalization) …