Decentralized RLS with data-adaptive censoring for regressions over large-scale networks

Zifeng Wang, Zheng Yu, Qing Ling, Dimitris Berberidis, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


The deluge of networked data motivates the development of algorithms for computation- and communication-efficient information processing. In this context, three data-adaptive censoring strategies are introduced to considerably reduce the computation and communication overhead of decentralized recursive least-squares solvers. The first relies on alternating minimization and the stochastic Newton iteration to minimize a network-wide cost, which discards observations with small innovations. In the resultant algorithm, each node performs local data-adaptive censoring to reduce computations while exchanging its local estimate with neighbors so as to consent on a network-wide solution. The communication cost is further reduced by the second strategy, which prevents a node from transmitting its local estimate to neighbors when the innovation it induces to incoming data is minimal. In the third strategy, not only transmitting, but also receiving estimates from neighbors is prohibited when data-adaptive censoring is in effect. For all strategies, a simple criterion is provided for selecting the threshold of innovation to reach a prescribed average data reduction. The novel censoring-based (C)D-RLS algorithms are proved convergent to the optimal argument in the mean-root deviation sense. Numerical experiments validate the effectiveness of the proposed algorithms in reducing computation and communication overhead.

Original languageEnglish (US)
Article number8264782
Pages (from-to)1634-1648
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number6
StatePublished - Mar 15 2018

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  • Decentralized estimation
  • data-adaptive censoring
  • networks
  • recursive least-squares (RLS)


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