Resilient Distributed Optimization for Multiagent Cyberphysical Systems

• 成果类型: Article
• 署名作者: Yemini, Michal; Nedic, Angelia; Goldsmith, Andrea J.; Gil, Stephanie
• 署名单位: Bar Ilan University; Arizona State University; Arizona State University-Tempe; Princeton University; Harvard University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3532791
• 发表日期: 2025
• 关键词: stochastic-approximation algorithms; Composite optimization; subgradient methods; consensus

摘要

This work focuses on the problem of distributed optimization in multiagent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points.