A Systematic Literature Review of Blockchain-based Federated Learning: Architectures, Applications and Issues

Dongkun Hou, Jie Zhang, Ka Lok Man, Jieming Ma, Zitian Peng

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

32 Citations (Scopus)

Abstract

Federal learning (FL) can realize a distributed training machine learning models in multiple devices while protecting their data privacy, but some defect still exists such as single point failure and lack of motivation. Blockchain as a distributed ledger can be utilized to provide a novel FL framework to address those issues. This paper aims to discuss how the blockchain technology is employed to compensate for shortcomings in FL. A systematic literature review is conducted to investigate existing FL problems and to summarize knowledge about the existing Blockchain-based FL (BFL). The differences among these collected BFL architectures are presented and discussed, and the applications of BFL are categorized and analyzed. Finally, some suggestions for future development and application of BFL are discussed.

Original languageEnglish
Title of host publication2021 2nd Information Communication Technologies Conference, ICTC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages302-307
Number of pages6
ISBN (Electronic)9780738142876
DOIs
Publication statusPublished - 7 May 2021
Event2nd Information Communication Technologies Conference, ICTC 2021 - Nanjing, China
Duration: 7 May 20219 May 2021

Publication series

Name2021 2nd Information Communication Technologies Conference, ICTC 2021

Conference

Conference2nd Information Communication Technologies Conference, ICTC 2021
Country/TerritoryChina
CityNanjing
Period7/05/219/05/21

Keywords

  • Blockchain
  • Federated Learning
  • Systematic Literature Review

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