4 papers
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…
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…
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…
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…