Learning Trust Over Directed Graphs in the Presence of Adversaries

• 成果类型: Article
• 署名作者: Akgun, Orhan Eren; Dayi, Arif Kerem; Nedic, Angelia; Gil, Stephanie
• 署名单位: Harvard University; Arizona State University; Arizona State University-Tempe
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3578424
• 发表日期: 2025
• 关键词: Distributed optimization; Reputation systems; management; networks

摘要

We investigate the problem of learning the trustworthiness of agents in a directed graph, where agents receive stochastic trust information about their in-neighbors. We consider a setting where an unknown subset of agents may be malicious and disseminate misinformation about the trustworthiness of other agents. We present a learning protocol where agents determine their trusted neighborhoods based on the stochastic trust information they receive, and leverage the opinions of their trusted neighbors to assess the trustworthiness of other agents. We show that, under mild assumptions about the communication network, agents can learn the trustworthiness of other agents almost surely. In addition, we establish an expected geometric convergence rate of the algorithm based on the properties of the stochastic trust values and the communication graph. Finally, we present numerical studies demonstrating that our convergence results hold in practice across various network topologies and with different numbers of malicious agents in the network.