Byzantine-Resilient Multiagent Optimization

• 成果类型: Article
• 署名作者: Su, Lili; Vaidya, Nitin H.
• 署名单位: Massachusetts Institute of Technology (MIT); Georgetown University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3008139
• 发表日期: 2021
• 页码: 2227-2233
• 关键词: Cost function; Consensus algorithm; Aggregates; computational modeling; Focusing; STANDARDS; Computational and artificial intelligence; Fault tolerance; fault tolerant control; Machine Learning; distributed computing; reliability

摘要

We consider the problem of multiagent optimization wherein an unknown subset of agents suffer Byzantine faults and thus behave adversarially. We assume that each agent i has a local cost function f(i), and the overarching goal of the good agents is to collaboratively minimize a global objective that properly aggregates these local cost functions. To the best of our knowledge, we are among the first to study Byzantine-resilient optimization where no central coordinating agent exists, and we are the first to characterize the structures of the convex coefficients of the achievable global objectives. Dealing with Byzantine faults is very challenging. For example, in contrast to fault-free networks, reaching Byzantine-resilient agreement even in the simplest setting is far from trivial. We take a step toward solving the proposed Byzantine-resilient multiagent optimization problem by focusing on scalar local cost functions. Our results might provide useful insights for the general local cost functions.