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Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning

Chenfei Nie,Qiang Li, Yuxin Yang,Yuede Ji,Binghui Wang

CoRR(2024)

Cited 0|Views12
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Abstract
Federated learning (FL) is an emerging distributed learning paradigm without sharing participating clients' private data. However, existing works show that FL is vulnerable to both Byzantine (security) attacks and data reconstruction (privacy) attacks. Almost all the existing FL defenses only address one of the two attacks. A few defenses address the two attacks, but they are not efficient and effective enough. We propose BPFL, an efficient Byzantine-robust and provably privacy-preserving FL method that addresses all the issues. Specifically, we draw on state-of-the-art Byzantine-robust FL methods and use similarity metrics to measure the robustness of each participating client in FL. The validity of clients are formulated as circuit constraints on similarity metrics and verified via a zero-knowledge proof. Moreover, the client models are masked by a shared random vector, which is generated based on homomorphic encryption. In doing so, the server receives the masked client models rather than the true ones, which are proven to be private. BPFL is also efficient due to the usage of non-interactive zero-knowledge proof. Experimental results on various datasets show that our BPFL is efficient, Byzantine-robust, and privacy-preserving.
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要点】:本文提出了BPFL,一个同时对抗Byzantine攻击和数据重构攻击的高效、可证明隐私保护的联邦学习方法。

方法】:BPFL结合了最新的Byzantine攻击鲁棒性联邦学习方法和零知识证明来验证客户端的有效性。

实验】:在多个数据集上的实验结果表明,BPFL既高效,又能抵御Byzantine攻击,同时保护隐私。