Differentially Private Distributed Optimization With an Event-Triggered Mechanism

被引:20
作者
Mao, Shuai [1 ]
Yang, Minglei [2 ]
Yang, Wen [2 ]
Tang, Yang [2 ]
Zheng, Wei Xing [3 ]
Gu, Juping [1 ]
Werner, Herbert [4 ]
机构
[1] Nantong Univ, Dept Elect Engn, Nantong 226019, Peoples R China
[2] East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China
[3] Western Sydney Univ, Sch Comp Data & Math Sci, Sydney, NSW 2751, Australia
[4] Hamburg Univ Technol, Inst Control Syst, D-21073 Hamburg, Germany
基金
中国国家自然科学基金;
关键词
Distributed optimization; event-triggered mechanism; differential privacy; ECONOMIC-DISPATCH; ALGORITHM;
D O I
10.1109/TCSI.2023.3266358
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This study concentrates on the differential private distributed optimization problem with an event-triggered mechanism, whose goals include preserving the privacy of agents' initial states and local cost functions and improving communication efficiency. A distributed event-triggered mechanism is integrated into the differentially private subgradient-push distributed optimization algorithm and then a new algorithm named as DP-ETSP is designed, where the real-time information propagation among agents is avoided. Additionally, under the proposed event-triggered mechanism, an analysis of mean-square consensus and optimality over time-varying directed networks is made when the added Laplace noises meet some specific decaying conditions. Convergence rate results are further established under a specific stepsize, which are equal to the rate of stochastic gradient-push algorithm without event-triggered communication. Moreover, the differential privacy preservation performance is analyzed and the rule for selecting privacy level is discussed. Finally, the feasibility and effectiveness of DP-ETSP are verified in two simulation cases.
引用
收藏
页码:2943 / 2956
页数:14
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