Distributed Gradient Tracking for Differentially Private Multi-Agent Optimization With a Dynamic Event-Triggered Mechanism

被引:10
作者
Yuan, Yang [1 ]
He, Wangli [1 ]
Du, Wenli [1 ]
Tian, Yu-Chu [2 ]
Han, Qing-Long [3 ]
Qian, Feng [1 ]
机构
[1] East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China
[2] Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld 4001, Australia
[3] Swinburne Univ Technol, Sch Sci Comp & Engn Technol, Melbourne, Vic 3122, Australia
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2024年 / 54卷 / 05期
基金
中国国家自然科学基金;
关键词
Optimization; Privacy; Heuristic algorithms; Convergence; Linear programming; Power system dynamics; Power system stability; Differential privacy; distributed optimization; dynamic event-triggered mechanism; TRACKING CONTROL; STABILITY; NETWORKS;
D O I
10.1109/TSMC.2024.3357253
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Distributed optimization achieves a minimized objective function through collaboration among distributed agents. Considering limited communication capabilities and privacy concerns, this article proposes a dynamic event-triggered differentially private gradient-tracking algorithm for distributed optimization. The communication requirement is reduced by event triggering, while the $\epsilon $ -differential privacy is guaranteed by perturbations on states and the tracking of the average gradient. The convergence point is uniquely determined by the noise injected to the tracking. Sufficient conditions for stepsizes are established theoretically to guarantee the convergence in mean and almost surely. Moreover, the theoretical privacy level is rigorously obtained and the positive effect of the event-triggered communication on the privacy is also discussed. Simulations are conducted for the classification of the dataset on the stability of a 4-node star power system to verify the theoretical findings.
引用
收藏
页码:3044 / 3055
页数:12
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