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.

翻译：本文研究鲁棒联邦学习问题，其中一组工作节点在中心服务器的协调下协同训练共享模型，同时面临能够执行任意且潜在恶意行为的拜占庭对抗者。为同时提升通信效率并增强对此类对抗者的鲁棒性，我们提出一种拜占庭容错的Nesterov加速联邦学习算法（Byrd-NAFL）。该算法将Nesterov动量机制无缝集成至联邦学习流程，并结合拜占庭容错聚合规则，在梯度被篡改的情况下实现快速且受保护的收敛。我们在非凸平滑损失函数下，基于对聚合梯度的宽松假设，建立了Byrd-NAFL的有限时间收敛性保证。大量数值实验验证了Byrd-NAFL的有效性，并在收敛速度、精度以及对多种拜占庭攻击策略的抵御能力方面展现出优于现有基准方法的性能。