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.

中文翻译：

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用于多任务估计的弹性分布式扩散

分布式扩散是一种强大的多任务状态估计算法，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，扩散提供了多种优势，包括对节点和链路故障的鲁棒性。在本文中，我们考虑了用于多任务估计的分布式扩散，其中网络代理必须通过处理流数据来估计不同但相关的感兴趣状态。通过利用用于扩散信息的自适应权重，我们开发了驱动正常代理收敛到攻击者选择的状态的攻击模型。攻击模型可用于平稳和非平稳状态估计。此外，我们开发了一种弹性分布式扩散算法，假设每个正常节点邻域中受损节点的数量以 $F$ 为界，并且我们表明可以以性能下降为代价获得弹性。最后，我们使用静态和非静态多目标定位来评估所提出的攻击模型和弹性分布式扩散算法。