NDLI: Convergence rates for cooperation in heterogeneous populations

Content Provider	IEEE Xplore Digital Library
Author	Bean, A. Kairouz, P. Singer, A.
Copyright Year	2012
Description	Author affiliation: University of Illinois at Urbana Champaign, 61801, USA (Bean, A.; Kairouz, P.; Singer, A.)
Abstract	We consider the problem of cooperative distributed estimation within a network of heterogeneous agents. In particular, we study the situation where each agent observes an independent stream of Bernoulli random variables, and the goal is for each to determine its own Bernoulli parameter. The agents of this population can be categorized into a small number of subgroups, where within each group the agents all have identical Bernoulli parameters. For a distributed algorithm based on consensus strategies, we examine the rate at which the agent's estimates converge to the correct values. We show that the expected squared error decreases nearly as fast as centralized ML estimation in a homogeneous population. In a heterogeneous population, we derive an approximation to the expected squared error, as a function of the number of observations. Finally, we present simulation results that compare the predicted expected squared error to that observed in the simulations.
Starting Page	531
Ending Page	534
File Size	237531
Page Count	4
File Format	PDF
ISBN	9781467350501
ISSN	10586393
e-ISBN	9781467350518
DOI	10.1109/ACSSC.2012.6489061
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	distributed signal processing gossip algorithms consensus diffusion adaptation distributed estimation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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