Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation

arXiv:2403.14905v1 Announce Type: cross
Abstract: In this article, we address the problem of federated learning in the presence of stragglers. For this problem, a coded federated learning framework has been proposed, where the central server aggregates gradients received from the non-stragglers and gradient computed from a privacy-preservation global coded dataset to mitigate the negative impact of the stragglers. However, when aggregating these gradients, fixed weights are consistently applied across iterations, neglecting the generation process of the global coded dataset and …

abstract article arxiv cs.cr cs.lg dataset eess.sp federated learning framework global gradient preservation privacy server type