Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
Byzantine-robust distributed optimization relies on robust aggregation rules to mitigate the influence of malicious Byzantine workers. Despite the proliferation of such rules, a unified convergence analysis framework that accommodates general data heterogeneity is lacking. In this work, we provide a thorough convergence theory of Byzantine-robust distributed stochastic gradient descent (SGD), analyzing variants both with and without local momentum. We establish the convergence rates for nonconvex smooth objectives and those satisfying the Polyak-Lojasiewicz condition under a general data heterogeneity assumption. Our analysis reveals that while stochasticity and data heterogeneity introdu...
| Authors | Boyuan Ruan, Xiaoyu Wang, Ya-Feng Liu |
|---|---|
| Primary category | math.OC |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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