Resilient Distributed Optimization

被引：1

作者：

Zhu, Jingxuan ^{[1
]}

Lin, Yixuan ^{[1
]}

Velasquez, Alvaro ^{[2
]}

Liu, Ji ^{[3
]}

机构：

[1] SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA

[2] Univ Colorado, Dept Comp Sci, Boulder, CO USA

[3] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY USA

来源：

2023 AMERICAN CONTROL CONFERENCE, ACC | 2023年

基金：

美国国家科学基金会;

关键词：

D O I：

10.23919/ACC55779.2023.10156564

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper considers a distributed optimization problem in the presence of Byzantine agents capable of introducing untrustworthy information into the communication network. A resilient distributed subgradient algorithm is proposed based on graph redundancy and objective redundancy. It is shown that the algorithm causes all non-Byzantine agents' states to asymptotically converge to the same optimal point under appropriate assumptions. A partial convergence rate result is also provided.

引用

下载

页码：1307 / 1312

页数：6

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