Decentralized Federated Learning: A Survey and Perspective

Liangqi Yuan, Ziran Wang, Lichao Sun, Philip S. Yu, Christopher G. Brinton

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead. Decentralized FL (DFL) is a decentralized network architecture that eliminates the need for a central server in contrast to centralized FL (CFL). DFL enables direct communication between clients, resulting in significant savings in communication resources. In this paper, a comprehensive survey and profound perspective are provided for DFL. First, a review of the methodology, challenges, and variants of CFL is conducted, laying the background of DFL. Then, a systematic and detailed perspective on DFL is introduced, including iteration order, communication protocols, network topologies, paradigm proposals, and temporal variability. Next, based on the definition of DFL, several extended variants and categorizations are proposed with state-of-the-art (SOTA) technologies. Lastly, in addition to summarizing the current challenges in the DFL, some possible solutions and future research directions are also discussed.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Internet of Things Journal
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Adaptation models
  • Data models
  • decentralized learning
  • Federated learning
  • Internet of Things
  • internet of things (IoT)
  • network
  • privacy preservation
  • Protocols
  • Servers
  • Surveys
  • Taxonomy

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