Jiayun Zhang

Taming Update Drift in Asynchronous Federated Learning via Orthogonal Calibration

Asynchronous federated learning improves the efficiency of conventional synchronous protocols by integrating updates as they arrive. However, asynchrony and data heterogeneity make learning objectives at global and local levels inherently inconsistent—global optimization trajectories can conflict with ongoing local upd...