5 papers
A Weighted Gradient Tracking Privacy-Preserving Method for Distributed Optimization
Furan Xie, Bing Liu, Li Chai
This paper investigates the privacy-preserving distributed optimization problem, aiming to protect agents' private information from potential attackers during the optimization proc…
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…
Enhancing Accuracy in Differentially Private Distributed Optimization Through Sensitivity Reduction
Furan Xie, Bing Liu, Li Chai
In this paper, we investigate the problem of differentially private distributed optimization. Recognizing that lower sensitivity leads to higher accuracy, we analyze the key factor…
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…
Cryptography-Based Privacy-Preserving Method for Distributed Optimization over Time-Varying Directed Graphs with Enhanced Efficiency
Bing Liu, Furan Xie, Li Chai
In this paper, we study the privacy-preserving distributed optimization problem, aiming to prevent attackers from stealing the private information of agents. For this purpose, we p…