Trading off communications bandwidth with accuracy in adaptive diffusion networks

Symeon Chouvardas, Konstantinos Slavakis, Sergios Theodoridis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, a novel algorithm for bandwidth reduction in adaptive distributed learning is introduced. We deal with diffusion networks, in which the nodes cooperate with each other, by exchanging information, in order to estimate an unknown parameter vector of interest. We seek for solutions in the framework of set theoretic estimation. Moreover, in order to reduce the required bandwidth by the transmitted information, which is dictated by the dimension of the unknown vector, we choose to project and work in a lower dimension Krylov subspace. This provides the benefit of trading off dimensionality with accuracy. Full convergence properties are presented, and experiments, within the system identification task, demonstrate the robustness of the algorithmic technique.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2048-2051
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • Adaptive distributed learning
  • Krylov subspaces
  • projections

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