Resilient Distributed Optimization Algorithm Against Adversarial Attacks
Resilient Distributed Optimization Algorithm Against Adversarial Attacks
复制标题
针对对抗性攻击的弹性分布式优化算法
DOI：
10.1109/tac.2019.2954363
复制
发表时间：
2020-10
影响因子：
6.8
通讯作者：
Qing-Guo Wang
中科院分区：
文献类型：
--
作者：
Chengcheng Zhao;Jianping He;Qing-Guo Wang
随着网络攻击成为网络物理系统面临的最具挑战性的威胁之一，研究网络攻击对分布式优化的影响，设计具有弹性的算法具有重要的理论价值和实用价值。大多数现有的作品都建立在这样的假设上，即所有正常代理都知道最大可容忍的攻击次数，这取决于网络的连通性。所有正常代理将使用最大攻击次数来决定是否将使用接收到的信息进行迭代。在这篇文章中，我们放松这一假设，并提出了一种新的弹性分布式优化算法。该算法利用可信代理不会被对抗性攻击破坏，并在原始图中形成连通支配集来约束对抗性攻击的影响。结果表明，在该算法下，所有正常和可信代理的局部变量收敛到相同的值。此外，最终的解决方案属于所有可信代理的局部成本函数的加权平均的最小化的凸集。给出了最终解与最优解之间距离的上界。数值结果证明了该算法的有效性。
As the cyber-attack is becoming one of the most challenging threats faced by cyber-physical systems, investigating the effect of cyber-attacks on distributed optimization and designing resilient algorithms are of both theoretical merits and practical values. Most existing works are established on the assumption that the maximum tolerable number of attacks, which depends on the network connectivity, is known by all normal agents. All normal agents will use the maximum number of attacks to decide whether the received information will be used for iterations. In this article, we relax this assumption and propose a novel resilient distributed optimization algorithm. The proposed algorithm exploits the trusted agents which cannot be compromised by adversarial attacks and form a connected dominating set in the original graph to constrain effects of adversarial attacks. It is shown that local variables of all normal and trusted agents converge to the same value under the proposed algorithm. Further, the final solution belongs to the convex set of minimizers of the weighted average of local cost functions of all trusted agents. The upper bound of the distance between the final solution and the optimal one has also been provided. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.
登录
查看更多内容
DOI：
10.1109/icca.2017.8003106
发表时间：
2017-07
期刊：
2017 13th IEEE International Conference on Control & Automation (ICCA)
影响因子：
--
作者：
Chengcheng Zhao;Jianping He;Qing-Guo Wang
通讯作者：
Chengcheng Zhao;Jianping He;Qing-Guo Wang
影响因子：
3
作者：
Ran Xin;U. Khan
通讯作者：
Ran Xin;U. Khan
DOI：
10.1109/icassp.2019.8683442
发表时间：
2019-05
期刊：
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
--
作者：
Nikhil Ravi;A. Scaglione;A. Nedić
通讯作者：
Nikhil Ravi;A. Scaglione;A. Nedić
DOI：
10.1109/tnet.2017.2780262
发表时间：
2018-02
期刊：
IEEE/ACM Transactions on Networking
影响因子：
--
作者：
Bei Liu;Wei Wang;Donghyun Kim;Yingshu Li;Sung-Sik Kwon;Yaolin Jiang
通讯作者：
Bei Liu;Wei Wang;Donghyun Kim;Yingshu Li;Sung-Sik Kwon;Yaolin Jiang
智能电网中基于共识的能源管理，具有传输损耗和定向通信
影响因子：
9.6
作者：
Chengcheng Zhao;Jianping He;Peng Cheng;Jiming Chen
通讯作者：
Jiming Chen