Blockchained on-device federated learning

Hyesung Kim, Jihong Park, Mehdi Bennis, Seong Lyun Kim

Research output: Contribution to journalArticlepeer-review

264 Citations (Scopus)


By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified. This enables on-device machine learning without any centralized training data or coordination by utilizing a consensus mechanism in blockchain. Moreover, we analyze an end-to-end latency model of BlockFL and characterize the optimal block generation rate by considering communication, computation, and consensus delays.

Original languageEnglish
Article number8733825
Pages (from-to)1279-1283
Number of pages5
JournalIEEE Communications Letters
Issue number6
Publication statusPublished - 2020 Jun

Bibliographical note

Funding Information:
Manuscript received May 6, 2019; accepted May 23, 2019. Date of publication June 10, 2019; date of current version June 10, 2020. This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government (MSIT) (No. 2018-0-00170, Virtual Presence in Moving Objects through 5G), Basic Science Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (NRF-2017R1A2A2A05069810), and the Mobile Edge Intelligence at Scale (ELLIS) Project at the University of Oulu. The associate editor coordinating the review of this letter and approving it for publication was V.-D. Nguyen. (Corresponding author: Seong-Lyun Kim.) H. Kim and S.-L. Kim are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:;

Publisher Copyright:
© 1997-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering


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