Federated Truth Inference over Distributed Crowdsourcing Platforms

Ming Hsun Yang, Gin Hao Liu, Y. W.Peter Hong

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

Abstract

This work examines the truth inference problem in a distributed crowdsourcing scenario. Labeling tasks are outsourced to workers associated with different platforms, and truth inference is to be performed without sharing the workers' individual responses with other platforms or the central coordinator. The reliability of the labels may vary over different workers and tasks, and is characterized by a Gaussian mixture model. A federated truth inference (FTI) algorithm is proposed based on a distributed implementation of the block expectation-maximization (EM) algorithm. The messages sent by each local platform to the central coordinator contain aggregates of workers' responses, instead of individual labels. The convergence of the FTI algorithm can be verified theoretically. A communication-efficient variant of the FTI scheme is also proposed by allowing the distributed platforms to perform multiple local EM computations before updating the global estimates at the central coordinator in each iteration. The effectiveness of our proposed schemes is demonstrated using both synthetic and real-world datasets.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5940-5944
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • Crowdsourcing
  • data fusion
  • distributed optimization
  • expectation-maximization
  • truth inference

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