arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.11911cs.MA
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多任务估计的弹性分布扩散

Resilient Distributed Diffusion for Multi-task Estimation

Jiani Li, Xenofon Koutsoukos

摘要:

分布式扩散是一种用于多任务状态估计的强大算法,它使网络代理能够与邻居交互以处理输入数据并在网络上扩散信息。与集中式方法相比,扩散提供了多个优点,包括对节点和链路故障的鲁棒性。在本文中,我们考虑分布式扩散的多任务估计网络代理必须估计不同的,但相关的感兴趣的状态,通过处理流数据。通过利用自适应权重用于扩散信息,我们开发的攻击模型,驱动正常代理收敛到攻击者选择的状态。攻击模型可用于稳态和非稳态状态估计。此外,我们开发了一个弹性的分布式扩散算法的假设下,妥协的节点的数量在每个正常节点的邻域是有界的$F$,我们表明,弹性可能会获得性能下降的成本。最后,我们评估了所提出的攻击模型和弹性分布式扩散算法使用静态和非静态多目标定位。

英文摘要:

Distributed diffusion is a powerful algorithm for multi-task state estimation which enables networked agents to interact with neighbors to process input data and diffuse information across the network. Compared to a centralized approach, diffusion offers multiple advantages that include robustness to node and link failures. In this paper, we consider distributed diffusion for multi-task estimation where networked agents must estimate distinct but correlated states of interest by processing streaming data. By exploiting the adaptive weights used for diffusing information, we develop attack models that drive normal agents to converge to states selected by the attacker. The attack models can be used for both stationary and non-stationary state estimation. In addition, we develop a resilient distributed diffusion algorithm under the assumption that the number of compromised nodes in the neighborhood of each normal node is bounded by $F$ and we show that resilience may be obtained at the cost of performance degradation. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and non-stationary multi-target localization.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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