Federated learning has exhibited vulnerabilities to Byzantine attacks, where the Byzantine attackers can send arbitrary gradients to a central server to destroy the convergence and performance of the global model. A wealth of robust AGgregation Rules (AGRs) have been proposed to defend against Byzantine attacks. However, Byzantine clients can still circumvent robust AGRs when data is non-Identically and Independently Distributed (non-IID). In this paper, we first reveal the root causes of performance degradation of current robust AGRs in non-IID settings: the curse of dimensionality and gradient heterogeneity. In order to address this issue, we propose GAS, a \shorten approach that can successfully adapt existing robust AGRs to non-IID settings. We also provide a detailed convergence analysis when the existing robust AGRs are combined with GAS. Experiments on various real-world datasets verify the efficacy of our proposed GAS. The implementation code is provided in https://github.com/YuchenLiu-a/byzantine-gas.

翻译：联邦学习已被发现易受拜占庭攻击影响，在此类攻击中，拜占庭攻击者可向中央服务器发送任意梯度，从而破坏全局模型的收敛性与性能。为防御拜占庭攻击，学界已提出大量鲁棒聚合规则。然而，当数据呈非独立同分布时，拜占庭客户端仍可规避这些鲁棒聚合规则。本文首先揭示了当前鲁棒聚合规则在非独立同分布场景下性能退化的根本原因：维度灾难与梯度异质性。为解决此问题，我们提出GAS——一种能成功将现有鲁棒聚合规则适配至非独立同分布场景的简化方法。我们还提供了现有鲁棒聚合规则与GAS结合时的详细收敛性分析。在多种真实世界数据集上的实验验证了所提出的GAS方法的有效性。实现代码已开源在https://github.com/YuchenLiu-a/byzantine-gas。