Jiayun Zhang

Taming Update Drift in Asynchronous Federated Learning via Orthogonal Calibration

OrthoFL is proposed, an orthogonal calibration framework that decouples global and local learning progress to reduce interference and consistently outperforms state-of-the-art asynchronous baselines under various delay patterns and heterogeneity scenarios.