Resilient Primal-Dual Optimization Algorithms for Distributed Resource Allocation
Resilient Primal-Dual Optimization Algorithms for Distributed Resource Allocation
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DOI：
10.1109/tcns.2020.3024485
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发表时间：
2021-03-01
影响因子：
4.2
通讯作者：
Alizadeh, Mahnoosh
中科院分区：
文献类型：
--
作者：
Turan, Berkay;Uribe, Cesar A.;Alizadeh, Mahnoosh
分布式多智能体资源分配算法可以在许多网络物理系统中提供集中式算法的隐私和可扩展性。然而，这些算法的分布式性质可以使这些系统容易受到中间人攻击，这可能导致不收敛和不可行的资源分配方案。在这篇文章中，我们提出了攻击弹性分布式算法的基础上原始对偶优化拜占庭攻击者存在于系统中。特别是，我们设计了攻击弹性的原始-对偶算法的静态和动态模拟攻击的鲁棒统计。对于静态模仿攻击，我们制定了鲁棒优化模型，并表明我们的算法保证收敛到鲁棒问题的最优解的邻域。另一方面，一个强大的优化模型是不需要的动态模拟攻击的情况下，我们能够设计一个算法，收敛到一个接近最优的解决方案的原始问题。我们分析了我们的算法的性能，通过理论和计算研究。
Distributed algorithms for multiagent resource allocation can provide privacy and scalability over centralized algorithms in many cyber-physical systems. However, the distributed nature of these algorithms can render these systems vulnerable to man-in-the-middle attacks that can lead to nonconvergence and infeasibility of resource allocation schemes. In this article, we propose attack-resilient distributed algorithms based on primal-dual optimization when Byzantine attackers are present in the system. In particular, we design attack-resilient primal-dual algorithms for static and dynamic impersonation attacks by means of robust statistics. For static impersonation attacks, we formulate a robustified optimization model and show that our algorithm guarantees convergence to a neighborhood of the optimal solution of the robustified problem. On the other hand, a robust optimization model is not required for the dynamic impersonation attack scenario and we are able to design an algorithm that is shown to converge to a near-optimal solution of the original problem. We analyze the performances of our algorithms through both theoretical and computational studies.