Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity

arXiv:2403.13374v1 Announce Type: cross
Abstract: This paper deals with federated learning (FL) in the presence of malicious Byzantine attacks and data heterogeneity. A novel Robust Average Gradient Algorithm (RAGA) is proposed, which leverages the geometric median for aggregation and can freely select the round number for local updating. Different from most existing resilient approaches, which perform convergence analysis based on strongly-convex loss function or homogeneously distributed dataset, we conduct convergence analysis for not only strongly-convex but also non-convex loss function …

aggregation algorithm arxiv attacks can cs.ai cs.cr cs.lg data deals federated federated learning local malicious novel presence resilient select