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.
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Manuscript received December 27, 2016; revised June 11, 2017, October 12, 2017, and December 11, 2017; accepted January 9, 2018. Date of publication January 23, 2018; date of current version February 7, 2018. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Joao Xavier. This work was supported in part by the NSF China under Grant 61573331, in part by the NSF Anhui under Grant 1608085QF130, and in part by the NSF under Grants 1442686, 1500713, and 1711471. This paper was presented in part at the 42nd IEEE International Conference on Acoustics, Speech, and Signal Processing, New Orleans, LA, USA, March 2017. (Corresponding author: Qing Ling.) Z. Wang and Z. Yu are with the Special Class for the Gifted Young, University of Science and Technology of China, Hefei 230026, China (e-mail: firstname.lastname@example.org; email@example.com).
- Decentralized estimation
- data-adaptive censoring
- recursive least-squares (RLS)