Fairness in Multiterminal Data Compression: Decomposition of Shapley Value

Ni Ding, David Smith, Thierry Rakotoarivelo, Parastoo Sadeghi

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    We consider the problem of how to attain fairness in the multiterminal data compression problem by a game-theoretic approach and present a decomposition method for obtaining the Shapley value, a fair source coding rate vector in the Slepian-Wolf achievable region. We model a discrete memoryless multiple random source (DMMS) by a coalitional game where the entropy function quantifies the cost incurred by the source coding rates in each coalition. In the typical case for which the game is decomposable, we show that the Shapley value can be obtained separately for each subgame. The complexity of this decomposition method is determined by the maximum size of subgames, which is strictly smaller than the total number of terminals in the DMMS and contributes to a considerable reduction in computational complexity. An experimental result demonstrates large complexity reduction when the number of terminals in the DMMS becomes large.

    Original languageEnglish
    Title of host publication2018 IEEE International Symposium on Information Theory, ISIT 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages886-890
    Number of pages5
    ISBN (Print)9781538647806
    DOIs
    Publication statusPublished - 15 Aug 2018
    Event2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States
    Duration: 17 Jun 201822 Jun 2018

    Publication series

    NameIEEE International Symposium on Information Theory - Proceedings
    Volume2018-June
    ISSN (Print)2157-8095

    Conference

    Conference2018 IEEE International Symposium on Information Theory, ISIT 2018
    Country/TerritoryUnited States
    CityVail
    Period17/06/1822/06/18

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