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Decentralized Learning for Wireless Communications and Networking

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Splitting Methods in Communication, Imaging, Science, and Engineering

Part of the book series: Scientific Computation ((SCIENTCOMP))

Abstract

This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternating-direction method of multipliers (ADMM) so as to gain the desired degree of parallelization. Without exchanging elements from the distributed training sets and keeping inter-node communications at affordable levels, the local (per-node) learners consent to the desired quantity inferred globally, meaning the one obtained if the entire training data set were centrally available. Impact of the decentralized learning framework to contemporary wireless communications and networking tasks is illustrated through case studies including target tracking using wireless sensor networks, unveiling Internet traffic anomalies, power system state estimation, as well as spectrum cartography for wireless cognitive radio networks.

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Authors and Affiliations

  1. University of Minnesota, 117 Pleasant Str., Minneapolis, MN, 55455, USA

    Georgios B. Giannakis

  2. University of Science and Technology of China, 443 Huangshan Road, Hefei, Anhui, 230027, China

    Qing Ling

  3. University of Rochester, 413 Hopeman Engineering Building, Rochester, NY, 14627, USA

    Gonzalo Mateos

  4. University of Texas at Arlington, 416 Yates Street, Arlington, TX, 76010, USA

    Ioannis D. Schizas

  5. University of Illinois at Urbana-Champaign, 4058 ECE Building, 306 N. Wright Street, Urbana, IL, 61801, USA

    Hao Zhu

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  1. Georgios B. Giannakis
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  2. Qing Ling
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  3. Gonzalo Mateos
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  4. Ioannis D. Schizas
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  5. Hao Zhu
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Correspondence to Georgios B. Giannakis .

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Editors and Affiliations

  1. Department of Mathematics, University of Houston, Houston, Texas, USA

    Roland Glowinski

  2. Department of Mathematics, UCLA, Los Angeles, California, USA

    Stanley J. Osher

  3. Department of Mathematics, UCLA, Los Angeles, California, USA

    Wotao Yin

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Giannakis, G.B., Ling, Q., Mateos, G., Schizas, I.D., Zhu, H. (2016). Decentralized Learning for Wireless Communications and Networking. In: Glowinski, R., Osher, S., Yin, W. (eds) Splitting Methods in Communication, Imaging, Science, and Engineering. Scientific Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-41589-5_14

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