Estimation of Individual Device Contributions for Incentivizing Federated Learning

Takayuki Nishio, Ryoichi Shinkuma, Narayan B. Mandayam

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728173078
DOI
出版ステータスPublished - 2020 12
外部発表はい
イベント2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China
継続期間: 2020 12 72020 12 11

出版物シリーズ

名前2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
国/地域Taiwan, Province of China
CityVirtual, Taipei
Period20/12/720/12/11

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ
  • ソフトウェア

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