Web: http://arxiv.org/abs/2205.05611

May 12, 2022, 1:11 a.m. | Truc Nguyen, Phuc Thai, Tre' R. Jeter, Thang N. Dinh, My T. Thai

cs.LG updates on arXiv.org arxiv.org

Despite the great potential of Federated Learning (FL) in large-scale
distributed learning, the current system is still subject to several privacy
issues due to the fact that local models trained by clients are exposed to the
central server. Consequently, secure aggregation protocols for FL have been
developed to conceal the local models from the server. However, we show that,
by manipulating the client selection process, the server can circumvent the
secure aggregation to learn the local models of a victim …

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