提出新型联邦学习聚合方法，有效抵御50%以上恶意客户端攻击。
FLTG: Byzantine-Robust Federated Learning via Angle-Based Defense and Non-IID-Aware Weighting
- 通过角度相似性过滤异常更新，结合动态参考客户端减少非独立同分布偏差。
- 在五种攻击下保持高鲁棒性，恶意客户端占比超50%时仍稳定收敛。
- 适合数据分布差异大且存在恶意节点的现实联邦学习场景。
联邦学习中模型聚合阶段的拜占庭攻击会通过恶意客户端更新破坏训练完整性。现有方法在高比例恶意客户端和非独立同分布数据下鲁棒性不足，导致准确率下降。为此，本文提出FLTG，一种融合角度防御与非独立同分布感知加权的新型聚合算法。FLTG首先利用服务器端干净数据集，通过ReLU截断余弦相似性筛选客户端，剔除方向偏差大的更新；随后基于历史全局模型动态选择参考客户端，将权重设置为与角度偏差成反比，并对更新幅度进行归一化以抑制恶意缩放。在多种复杂度数据集上，针对五类经典攻击的评估表明，FLTG在极端偏置场景下优于当前最优方法，且在超过50%恶意客户端比例下仍保持鲁棒性。
原文摘要 · Abstract (English)
Byzantine attacks during model aggregation in Federated Learning (FL) threaten training integrity by manipulating malicious clients' updates. Existing methods struggle with limited robustness under high malicious client ratios and sensitivity to non-i.i.d. data, leading to degraded accuracy. To address this, we propose FLTG, a novel aggregation algorithm integrating angle-based defense and dynamic reference selection. FLTG first filters clients via ReLU-clipped cosine similarity, leveraging a server-side clean dataset to exclude misaligned updates. It then dynamically selects a reference client based on the prior global model to mitigate non-i.i.d. bias, assigns aggregation weights inversely proportional to angular deviations, and normalizes update magnitudes to suppress malicious scaling. Evaluations across datasets of varying complexity under five classic attacks demonstrate FLTG's superiority over state-of-the-art methods under extreme bias scenarios and sustains robustness with a higher proportion(over 50%) of malicious clients.
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