Resilience to Malicious Activity in Distributed Optimization for Cyberphysical Systems
Resilience to Malicious Activity in Distributed Optimization for Cyberphysical Systems
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DOI：
10.1109/cdc51059.2022.9992416
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
M. Yemini;A. Nedić;S. Gil;A. Goldsmith
中科院分区：
文献类型：
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作者：
M. Yemini;A. Nedić;S. Gil;A. Goldsmith
增强分布式网络中的恶意代理的弹性是一个重要的问题，许多关键的理论结果和应用需要进一步的发展和表征。这项工作开发了一种新的算法和分析框架，用于在分布式优化问题中实现对恶意代理的弹性，其中合法代理的动态受到其从邻近代理和其自身的自助目标函数接收的值的影响。我们表明，通过利用代理之间的信任的随机值，即使在恶意代理的存在下，也可以恢复收敛到系统的全局最优点。此外，我们提供了预期的收敛速度保证的形式上界的预期平方距离的最佳值。最后，我们提出的数值结果，验证了分析收敛保证，我们在本文中提出的恶意代理人的大多数代理在网络中。
Enhancing resilience in distributed networks in the face of malicious agents is an important problem for which many key theoretical results and applications require further development and characterization. This work develops a new algorithmic and analytical framework for achieving resilience to malicious agents in distributed optimization problems where a legitimate agent’s dynamic is influenced by the values it receives from neighboring agents and its own self-serving target function. We show that by utilizing stochastic values of trust between agents it is possible to recover convergence to the system’s global optimal point even in the presence of malicious agents. Additionally, we provide expected convergence rate guarantees in the form of an upper bound on the expected squared distance to the optimal value. Finally, we present numerical results that validate the analytical convergence guarantees we present in this paper even when the malicious agents are the majority of agents in the network.