Estimation of Individual Device Contributions for Incentivizing Federated Learning

Takayuki Nishio, Ryoichi Shinkuma, Narayan B. Mandayam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


Federated learning (FL) is an emerging technique used to collaboratively train a machine-learning model using the data and computation resources of mobile devices without exposing private or sensitive user data. Appropriate incentive mechanisms that motivate the data and mobile-device owner to participate in FL is key to building a sustainable platform. However, it is difficult to evaluate the contribution levels of participants to determine appropriate rewards without large computation and communication overhead. This paper proposes a computation- A nd communication-efficient method of estimating participants contribution levels. The proposed method requires a single FL training process, which significantly reduces overhead. Performance evaluations are done using the MNIST dataset, showing that the proposed method estimates participant contributions accurately with 46-49% less computation overhead and no communication overhead, as compared to a naive estimation method.

Original languageEnglish
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173078
Publication statusPublished - 2020 Dec
Externally publishedYes
Event2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: 2020 Dec 72020 Dec 11

Publication series

Name2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings


Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
Country/TerritoryTaiwan, Province of China
CityVirtual, Taipei


  • Contribution Estimation
  • Contribution Metric
  • Federate Learning
  • Incentive Mechanism

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software


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