Tackling heavy-tailed noise in distributed estimation: Asymptotic performance and tradeoffs
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917356279693312 |
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| author | Bajovic, Dragana Jakovetic, Dusan Kar, Soummya Vukovic, Manojlo |
| author_facet | Bajovic, Dragana Jakovetic, Dusan Kar, Soummya Vukovic, Manojlo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20728 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tackling heavy-tailed noise in distributed estimation: Asymptotic performance and tradeoffs Bajovic, Dragana Jakovetic, Dusan Kar, Soummya Vukovic, Manojlo Information Theory Optimization and Control 93E10, 93E35, 60G35, 94A13, 62M05 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. |
| title | Tackling heavy-tailed noise in distributed estimation: Asymptotic performance and tradeoffs |
| topic | Information Theory Optimization and Control 93E10, 93E35, 60G35, 94A13, 62M05 |
| url | https://arxiv.org/abs/2603.20728 |