マルチエージェントサイバーフィジカルシステムにおけるレジリエント分散最適化
Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems
悪意あるエージェントが存在する分散最適化問題に対し、エージェント間の信頼の確率的値を活用することで、平均およびほぼ確実に真の大域最適解への収束を回復する新たなアルゴリズムと解析枠組みを提案した。
著者: Michal Yemini, Angelia Nedić, Andrea J. Goldsmith, Stephanie Gil
分類: cs.RO, cs.SY, eess.SP, eess.SY
原文アブストラクト
This work focuses on the problem of distributed optimization in multi-agent 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.
