提出新方法抵御恶意客户端攻击，提升联邦学习鲁棒性。
FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning
- 通过加权投票确定参数梯度的共识方向
- 仅聚合与共识方向一致的稀疏梯度，降低攻击影响
- 在3个数据集上对7种攻击均表现更优，适合高安全需求场景
联邦学习中对抗拜占庭客户端的常见防御策略是使用鲁棒聚合机制。尽管现有方法在理论上保证收敛且对部分攻击有效，但强攻击仍可破坏其鲁棒性并导致训练崩溃。为此，本文提出FedSECA，一种基于符号选举与坐标级梯度聚合的方法，能更好抵御全知攻击者。该方法包含两个核心模块：首先，共识比诱导符号选举（CRISE）通过加权投票确定每个参数梯度的共识方向，客户端权重由新颖的共识比决定，量化各客户端梯度符号的一致性；其次，鲁棒坐标级聚合（RoCA）策略仅在梯度方向与共识方向一致时，才对低方差稀疏梯度进行聚合。在3个数据集和3种模型架构上，对比10种现有鲁棒聚合器，在7种拜占庭攻击下验证结果表明，多数现有方法对某些攻击失效，而FedSECA始终表现更优。代码已开源。
原文摘要 · Abstract (English)
One of the most common defense strategies against Byzantine clients in federated learning (FL) is to employ a robust aggregator mechanism that makes the training more resilient. While many existing Byzantine robust aggregators provide theoretical convergence guarantees and are empirically effective against certain categories of attacks, we observe that certain high-strength attacks can subvert the robust aggregator and collapse the training. To overcome this limitation, we propose a method called FedSECA for robust Sign Election and Coordinate-wise Aggregation of gradients in FL that is less susceptible to malicious updates by an omniscient attacker. The proposed method has two main components. The Concordance Ratio Induced Sign Election(CRISE) module determines the consensus direction (elected sign) for each individual parameter gradient through a weighted voting strategy. The client weights are assigned based on a novel metric called concordance ratio, which quantifies the degree of sign agreement between the client gradient updates. Based on the elected sign, a Robust Coordinate-wise Aggregation(RoCA) strategy is employed, where variance-reduced sparse gradients are aggregated only if they are in alignment with the corresponding elected sign. We compare our proposed FedSECA method against 10 robust aggregators under 7 Byzantine attacks on 3 datasets and architectures. The results show that existing robust aggregators fail for at least some attacks, while FedSECA exhibits better robustness. Code - https://github.com/JosephGeoBenjamin/FedSECA-ByzantineTolerance
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