Federated learning provides a privacy-aware learning framework by enabling participants to jointly train models without exposing their private data. However, federated learning has exhibited vulnerabilities to Byzantine attacks, where the adversary aims to destroy the convergence and performance of the global model. Meanwhile, we observe that most existing robust AGgregation Rules (AGRs) fail to stop the aggregated gradient deviating from the optimal gradient (the average of honest gradients) in the non-IID setting. We attribute the reason of the failure of these AGRs to two newly proposed concepts: identification failure and integrity failure. The identification failure mainly comes from the exacerbated curse of dimensionality in the non-IID setting. The integrity failure is a combined result of conservative filtering strategy and gradient heterogeneity. In order to address both failures, we propose GAIN, a gradient decomposition scheme that can help adapt existing robust algorithms to heterogeneous datasets. We also provide convergence analysis for integrating existing robust AGRs into GAIN. Experiments on various real-world datasets verify the efficacy of our proposed GAIN.

针对联邦学习中的拜占庭攻击和不同数据分布下聚合规则的局限性问题，提出一种名为GAIN的梯度分解方案，能够适应异构数据集并改进现有鲁棒算法的并行训练方法。

使用梯度分解增强联邦学习的拜占庭容错能力