Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity

• URL: http://arxiv.org/abs/2503.16337v1
• Date: Thu, 20 Mar 2025 16:56:06 GMT
• Title: Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity
• Authors: Qiankun Shi, Jie Peng, Kun Yuan, Xiao Wang, Qing Ling,
• Abstract summary: In this paper, we establish tight lower bounds for Byzantine-robust distributed first-order optimization methods.<n>To fill the gap, we leverage the techniques of Nesterov's acceleration reduction to develop novel Byzantine-robust distributed optimization methods.
• Score: 37.14123597310607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we establish tight lower bounds for Byzantine-robust distributed first-order stochastic optimization methods in both strongly convex and non-convex stochastic optimization. We reveal that when the distributed nodes have heterogeneous data, the convergence error comprises two components: a non-vanishing Byzantine error and a vanishing optimization error. We establish the lower bounds on the Byzantine error and on the minimum number of queries to a stochastic gradient oracle required to achieve an arbitrarily small optimization error. Nevertheless, we identify significant discrepancies between our established lower bounds and the existing upper bounds. To fill this gap, we leverage the techniques of Nesterov's acceleration and variance reduction to develop novel Byzantine-robust distributed stochastic optimization methods that provably match these lower bounds, up to logarithmic factors, implying that our established lower bounds are tight.

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