AI RESEARCH
FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation
arXiv CS.LG
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ArXi:2601.22204v2 Announce Type: replace Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client participation errors, the last of which is the most pervasive but remains insufficiently addressed in current literature. In this paper, we propose FedAdaVR, a novel FL algorithm aimed at solving heterogeneity issues caused by sporadic client participation by incorporating an adaptive optimiser with a variance reduction technique.