Adaptive robust distributed learning in diffusion sensor networks

Symeon Chouvardas, Konstantinos Slavakis, Sergios Theodoridis

Research output: Contribution to journalArticlepeer-review

126 Scopus citations


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
Issue number10
StatePublished - Oct 1 2011


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

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