We present an algorithm for distributed estimation of an unknown vector parameter $\boldsymbolθ^\ast \in {\mathbb R}^M$ in the presence of heavy-tailed observation and communication noises. Heavy-tailed noises frequently appear, e.g., in densely deployed Internet of Things (IoT) or wireless sensor network systems. The presented algorithm falls within the class of \emph{consensus+innovation} estimators and combats the effect of the heavy-tailed noises by adding general nonlinearities in the consensus and innovations update parts. We present results on almost sure convergence and asymptotic normality of the estimator. In addition, we provide novel analytical studies that reveal interesting tradeoffs between the system noises and the underlying network topology.

翻译：我们提出了一种在观测和通信噪声具有重尾特性时，对未知向量参数 $\boldsymbolθ^\ast \in {\mathbb R}^M$ 进行分布式估计的算法。重尾噪声常见于密集部署的物联网或无线传感器网络系统中。所提算法属于 \emph{共识+创新} 估计器类别，通过在共识与创新更新部分引入一般非线性函数来抑制重尾噪声的影响。我们给出了该估计器的几乎必然收敛性与渐近正态性结果。此外，我们提供了新颖的分析研究，揭示了系统噪声与底层网络拓扑之间的有趣权衡。