Robust Decentralized Online Optimization Against Malicious Agents

Dacheng Wen, Yupeng Li, Xiaoxi Zhang, Francis C. M. Lau

Abstract: Decentralized online optimization, a pivotal paradigm in machine learning, involves multiple agents making online decisions cooperatively in a decentralized network. Despite its outstanding capabilities in processing large-scale streaming data, the ubiquitous existence of malicious agents, capable of disseminating arbitrary information among their neighbors and undetectable a priori, poses a severe threat to the reliability and efficacy of existing decentralized online optimization solutions. In response to the above critical vulnerability in practice, we take the first step to properly address the threat posed by malicious agents. We propose ROOO, a novel robust decentralized online optimization algorithm, specifically designed to counteract the detrimental impact of malicious agents. Our theoretical analysis shows that the regret bound of ROOO is sub-linear, indicating that, over time, its performance progressively approximates that of an offline oracle operating with the benefit of hindsight. Empirical evaluations in two networking applications, including opportunistic channel selection and mobile crowdsensing, further validate our theoretical results and demonstrate the competitiveness of ROOO compared to several advanced baselines.