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FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
April 19, 2024, 4:41 a.m. | Sungwon Han, Hyeonho Song, Sungwon Park, Meeyoung Cha
cs.LG updates on arXiv.org arxiv.org
Abstract: Federated learning combines local updates from clients to produce a global model, which is susceptible to poisoning attacks. Most previous defense strategies relied on vectors derived from projections of local updates on a Euclidean space; however, these methods fail to accurately represent the functionality and structure of local models, resulting in inconsistent performance. Here, we present a new paradigm to defend against poisoning attacks in federated learning using functional mappings of local models based on …
abstract arxiv attacks cs.cr cs.lg data defense federated learning free global however intermediate poisoning attacks space strategies type updates vectors
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