Yangming Zhao

POCS: phased optimal client selection for federated learning

Abstract Traditional federated learning suffers from excessive communication overhead due to iterative model exchanges, long training time caused by data heterogeneity, and vulnerability to adversarial attacks due to gradient leakage risks. But if one strategically selects some specific clients for training during diff...