Resilient Penalty Function Method for Distributed Constrained Optimization under Byzantine Attack

被引：0

作者：

Xu, Chentao ^{[1
]}

Liu, Qingshan ^{[2
,3
]}

Huang, Tingwen ^{[4
]}

机构：

[1] School of Cyber Science and Engineering, Southeast University, Nanjing,210096, China

[2] School of Mathematics, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing,210096, China

[3] Purple Mountain Laboratories, Nanjing,211111, China

[4] Department of Science Program, Texas A&M University at Qatar, Doha,23874, Qatar

来源：

Information Sciences | 2022年 / 596卷

基金：

中国国家自然科学基金;

关键词：

Byzantine attacks - Distributed constrained optimisation - Distributed optimization - Objective functions - Parallel com- puting - Penalty function - Penalty function methods - Performance - Privacy protection - Statics and dynamics;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Distributed optimization algorithms have the advantages of privacy protection and parallel computing. However, the distributed nature of these algorithms makes the system vulnerable to external attacks. This paper presents two penalty function based resilient algorithms for constrained distributed optimization under static and dynamic attacks. The objective function of the optimization problem is extended to nonsmooth ones and the convergence of the proposed algorithms in this case are proved under some mild conditions. Simulation experiments are performed and compared with some existing resilient primal-dual optimization algorithms using median-based mean estimator. For static attack, the proposed algorithm has better performance and faster convergence rate in the simulation experiments. For dynamic attack, the proposed algorithm has better performance and robustness in the simulation experiments, which illustrate that the proposed algorithms are more effective. © 2022 Elsevier Inc.

引用

页码：362 / 379