Hua Kun

URP-FL: Robust and Personalized Federated Learning under Heterogeneous and Adversarial Conditions

Federated learning is appealing for privacy-sensitive network systems, yet its practical deployment remains hindered by the following three recurring challenges: (1) client drift under non-IID data, (2) vulnerability to corrupted updates, and (3) the communication cost of repeated model exchange. Most existing approach...