On the Efficiency of Privacy Attacks in Federated Learning

arXiv:2404.09430v1 Announce Type: cross
Abstract: Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recovering private data, making the privacy attack impractical in real applications. In this study, we examine privacy attacks from the perspective of efficiency and propose a framework for improving the Efficiency of Privacy Attacks in Federated Learning (EPAFL). We make three …

arxiv attacks cs.cr cs.lg efficiency federated learning privacy type