FLeX: Trading edge computing resources for federated learning via blockchain

Yang Deng, Tao Han, Ning Zhang

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

10 Scopus citations

Abstract

Federated learning (FL) algorithms provide privileges in personal data protection and information islands elimination for distributed machine learning. As an increasing number of edge devices connected in networks, we still see a lot of computing resources and data remaining underutilized and there is no platform for users to trade FL tasks. In this demonstration, we propose a blockchain-based federated learning application trading platform called FLeX, on which users can buy and sell computing resources for training machine learning models with no sacrifice of data privacy. We design FLeX in a highly distributed and scalable manner. We separate the data plane and control plane in the platform. In FLeX, trading mechanisms and FL algorithms are deployed in smart contracts of the blockchain. Control messages and trading information are well protected in the blockchain. With FLeX, we realize a distributed trading platform for executing FL tasks.

Original languageEnglish (US)
Title of host publicationIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665404433
DOIs
StatePublished - May 10 2021
Externally publishedYes
Event2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 - Virtual, Online
Duration: May 9 2021May 12 2021

Publication series

NameIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021

Conference

Conference2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
CityVirtual, Online
Period5/9/215/12/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Keywords

  • Blockchain
  • Federated learning

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