Analyzing the Impact of Partial Sharing on the Resilience of Online Federated Learning Against Model Poisoning Attacks

arXiv:2403.13108v1 Announce Type: new
Abstract: We scrutinize the resilience of the partial-sharing online federated learning (PSO-Fed) algorithm against model-poisoning attacks. PSO-Fed reduces the communication load by enabling clients to exchange only a fraction of their model estimates with the server at each update round. Partial sharing of model estimates also enhances the robustness of the algorithm against model-poisoning attacks. To gain better insights into this phenomenon, we analyze the performance of the PSO-Fed algorithm in the presence of Byzantine clients, …

abstract algorithm arxiv attacks communication cs.cr cs.dc cs.lg eess.sp enabling fed federated learning impact poisoning attacks pso resilience server type update