Web: http://arxiv.org/abs/2204.13256

April 29, 2022, 1:20 a.m. | Wei Wan, Shengshan Hu, Jianrong Lu, Leo Yu Zhang, Hai Jin, Yuanyuan He

cs.CR updates on arXiv.org arxiv.org

Federated learning (FL) enables multiple clients to collaboratively train an
accurate global model while protecting clients' data privacy. However, FL is
susceptible to Byzantine attacks from malicious participants. Although the
problem has gained significant attention, existing defenses have several flaws:
the server irrationally chooses malicious clients for aggregation even after
they have been detected in previous rounds; the defenses perform ineffectively
against sybil attacks or in the heterogeneous data setting.

To overcome these issues, we propose MAB-RFL, a new method …


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