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Distributed estimation and learning over heterogeneous networks

Rahimian, MA and Jadbabaie, A (2017) Distributed estimation and learning over heterogeneous networks. In: UNSPECIFIED.

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We consider several estimation and learning problems that networked agents face when making decisions given their uncertainty about an unknown variable. Our methods are designed to efficiently deal with heterogeneity in both size and quality of the observed data, as well as heterogeneity over time (intermittence). The goal of the studied aggregation schemes is to efficiently combine the observed data that is spread over time and across several network nodes, accounting for all the network heterogeneities. Moreover, we require no form of coordination beyond the local neighborhood of every network agent or sensor node. The three problems that we consider are (i) maximum likelihood estimation of the unknown given initial data sets, (ii) learning the true model parameter from streams of data that the agents receive intermittently over time, and (iii) minimum variance estimation of a complete sufficient statistic from several data points that the networked agents collect over time. In each case, we rely on an aggregation scheme to combine the observations of all agents; moreover, when the agents receive streams of data over time, we modify the update rules to accommodate the most recent observations. In every case, we demonstrate the efficiency of our algorithms by proving convergence to the globally efficient estimators given the observations of all agents. We supplement these results by investigating the rate of convergence and providing finite-time performance guarantees.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Rahimian, MARAHIMIAN@pitt.eduRAHIMIAN0000-0001-9384-1041
Jadbabaie, A
Date: 10 February 2017
Date Type: Publication
Journal or Publication Title: 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016
Page Range: 1314 - 1321
Event Type: Conference
DOI or Unique Handle: 10.1109/allerton.2016.7852386
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Refereed: Yes
ISBN: 9781509045495
Date Deposited: 17 Aug 2020 16:58
Last Modified: 09 Apr 2021 00:55


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