Sybil attacks detection for dynamic environment in federated learning
Lihuang Lin,
Xi Zhu,
Junbo Wang
Abstract:Federated learning can utilize its distributed structure to protect data privacy security of clients and improve efficiency of machine learning. However, its distributed framework also make itself be susceptible to sybil attacks. While previous research has already proposed defense methods to address this issue, they often fail to guarantee effective performance in a dynamic federated learning system, where some clients dynamically join in and out. To tackle this problem, our paper introduces a novel defense m… Show more
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