5 papers
Fully Distributed State Estimation for Multi-agent Systems and its Application in Cooperative Localization
Shuaiting Huang, Haodong Jiang, Chengcheng Zhao +2
In this paper, we investigate the distributed state estimation problem for a continuous-time linear multi-agent system (MAS) composed of agents and monitored by the ag…
ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning
Bing Liu, Chengcheng Zhao, Li Chai +2
Jointly addressing Byzantine attacks and privacy leakage in distributed machine learning (DML) has become an important issue. A common strategy involves integrating Byzantine-resil…
Near-Optimal Reinforcement Learning with Shuffle Differential Privacy
Shaojie Bai, Mohammad Sadegh Talebi, Chengcheng Zhao +2
Reinforcement learning (RL) is a powerful tool for sequential decision-making, but its application is often hindered by privacy concerns arising from its interaction data. This cha…
Privacy-Preserving Resilient Vector Consensus
Bing Liu, Chengcheng Zhao, Li Chai +2
This paper studies privacy-preserving resilient vector consensus in multi-agent systems against faulty agents, where normal agents can achieve consensus within the convex hull of t…
Heuristic Learning for Co-Design Scheme of Optimal Sequential Attack
Xiaoyu Luo, Haoxuan Pan, Chongrong Fang +3
This paper considers a novel co-design problem of the optimal \textit{sequential} attack, whose attack strategy changes with the time series, and in which the \textit{sequential} a…