提升联邦学习抗恶意攻击能力，加速模型收敛。
Nesterov-Accelerated Robust Federated Learning Over Byzantine Adversaries
- 融合Nesterov动量与鲁棒聚合，兼顾效率与安全。
- 在非凸损失下实现有限步收敛，对梯度污染更鲁棒。
- 适合高风险场景下的分布式模型训练，如金融、医疗。
我们研究了存在拜占庭敌手的鲁棒联邦学习，即一群工作者在中心服务器协调下协作训练共享模型，而拜占庭敌手可能实施任意且潜在恶意的行为。为同时提升通信效率并增强对这类敌手的鲁棒性，我们提出一种拜占庭鲁棒的Nesterov加速联邦学习（Byrd-NAFL）算法。Byrd-NAFL将Nesterov动量无缝融入联邦学习过程，并结合拜占庭鲁棒聚合规则，在梯度被污染的情况下仍能实现快速且安全的收敛。我们在非凸且光滑损失函数下建立了Byrd-NAFL的有限时间收敛保证，对聚合梯度的假设更为宽松。大量数值实验验证了Byrd-NAFL的有效性，并证明其在收敛速度、准确率及对多种拜占庭攻击策略的鲁棒性方面优于现有基准。
原文摘要 · Abstract (English)
We investigate robust federated learning, where a group of workers collaboratively train a shared model under the orchestration of a central server in the presence of Byzantine adversaries capable of arbitrary and potentially malicious behaviors. To simultaneously enhance communication efficiency and robustness against such adversaries, we propose a Byzantine-resilient Nesterov-Accelerated Federated Learning (Byrd-NAFL) algorithm. Byrd-NAFL seamlessly integrates Nesterov's momentum into the federated learning process alongside Byzantine-resilient aggregation rules to achieve fast and safeguarding convergence against gradient corruption. We establish a finite-time convergence guarantee for Byrd-NAFL under non-convex and smooth loss functions with relaxed assumption on the aggregated gradients. Extensive numerical experiments validate the effectiveness of Byrd-NAFL and demonstrate the superiority over existing benchmarks in terms of convergence speed, accuracy, and resilience to diverse Byzantine attack strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。