Adaptive robust distributed learning in diffusion sensor networks

Symeon Chouvardas, Konstantinos Slavakis, Sergios Theodoridis

Research output: Contribution to journalArticlepeer-review

144 Scopus citations

Abstract

In this paper, the problem of adaptive distributed learning in diffusion networks is considered. The algorithms are developed within the convex set theoretic framework. More specifically, they are based on computationally simple geometric projections onto closed convex sets. The paper suggests a novel combine-project-adapt protocol for cooperation among the nodes of the network; such a protocol fits naturally with the philosophy that underlies the projection-based rationale. Moreover, the possibility that some of the nodes may fail is also considered and it is addressed by employing robust statistics loss functions. Such loss functions can easily be accommodated in the adopted algorithmic framework; all that is required from a loss function is convexity. Under some mild assumptions, the proposed algorithms enjoy monotonicity, asymptotic optimality, asymptotic consensus, strong convergence and linear complexity with respect to the number of unknown parameters. Finally, experiments in the context of the system-identification task verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been developed for adaptive distributed learning.

Original languageEnglish (US)
Article number5948418
Pages (from-to)4692-4707
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume59
Issue number10
DOIs
StatePublished - Oct 2011
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received November 09, 2010; revised March 16, 2011 and June 07, 2011; accepted June 18, 2011. Date of publication July 12, 2011; date of current version September 14, 2011. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Hideaki Sakai. This material was based on work supported by “Hrakleitos II” grant co-financed by national (Greek General Secretariat for Research and Technology) and EU funds.

Keywords

  • Adaptive filtering
  • adaptive projected subgradient method
  • consensus
  • diffusion networks
  • distributed learning

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