Nesterov-Accelerated Robust Federated Learning Over Byzantine Adversaries

Abstract--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. T o 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. As a promising paradigm for privacy-preserving distributed learning, federated learning (FL) leverages the parallel computational capabilities of user terminals to learn from decentralized data with the orchestration of a central server. Since its inception [1], [2], FL has been proliferating across diverse application scenarios, e.g., healthcare [3], [4], mobile edge [5], [6], and autonomous driving [7], [8]. Despite the merits in preserving user privacy, vanilla FL paradigm is still facing two major challenges, namely, Byzantine resilience [9], [10] and communication efficiency [11]. To robustify the FL paradigm, Byzantine-resilient aggregation rules, e.g., Krum [10], the component-wise median (CwMed) [15], Bulyan [16], and geometric median (GeoMed) [17], are designed to enhance the trustworthiness and reliability of the FL paradigm. Another major challenge in FL lies in enhancing communication efficiency. Current communication-efficient FL algorithms can be broadly classified into three categories: (i) communication frequency reduction [18], [19], [20], [21], [22], [12], (ii) exchanged information compression [23], [24], [25], [6], and (iii) iteration reduction [20], [26], [27], [28].