Towards blockchain-based reputation-aware federated learning

Muhammad Habib Ur Rehman, Khaled Salah, Ernesto Damiani, Davor Svetinovic

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

99 Scopus citations

Abstract

Federated learning (FL) is the collaborative machine learning (ML) technique whereby the devices collectively train and update a shared ML model while preserving their personal datasets. FL systems solve the problems of communication-efficiency, bandwidth-optimization, and privacy-preservation. Despite the potential benefits of FL, one centralized shared ML model across all the devices produce coarse-grained predictions which, in essence, are not required in many application areas involving personalized prediction services. In this paper, we present a novel concept of fine-grained FL to decentralize the shared ML models on the edge servers. We then present a formal extended definition of fine-grained FL process in mobile edge computing systems. In addition, we define the core requirements of fine-grained FL systems including personalization, decentralization, fine-grained FL, incentive mechanisms, trust, activity monitoring, heterogeneity and context-awareness, model synchronization, and communication and bandwidth-efficiency. Moreover, we present the concept of blockchain-based reputation-aware fine-grained FL in order to ensure trustworthy collaborative training in mobile edge computing systems. Finally, we perform the qualitative comparison of proposed approach with state-of-the-art related work and found some promising initial results.

Original languageBritish English
Title of host publicationIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages183-188
Number of pages6
ISBN (Electronic)9781728186955
DOIs
StatePublished - Jul 2020
Event2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020 - Toronto, Canada
Duration: 6 Jul 20209 Jul 2020

Publication series

NameIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

Conference

Conference2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
Country/TerritoryCanada
CityToronto
Period6/07/209/07/20

Keywords

  • Blockchain
  • Federated learning
  • Machine learning
  • Mobile edge computing
  • Reputation
  • Trust

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