Parameter-Efficient Personalized Federated Learning for Accurate Cellular Traffic Prediction
This paper proposes federated clustering with adaptive personalization (FedCAP), a parameter-efficient personalized FL framework that separates cluster-level representation learning from client-level adaptation.