University of Oulu

H. Kim, J. Park, M. Bennis and S. Kim, "Blockchained On-Device Federated Learning," in IEEE Communications Letters, vol. 24, no. 6, pp. 1279-1283, June 2020, doi: 10.1109/LCOMM.2019.2921755

Blockchained on-device federated learning

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Author: Kim, Hyesung1; Park, Jihong2; Bennis, Mehdi2;
Organizations: 1School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
2Centre for Wireless Communications, University of Oulu, 4500 Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2019-12-09


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.

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Series: IEEE communications letters
ISSN: 1089-7798
ISSN-E: 2373-7891
ISSN-L: 1089-7798
Volume: 24
Issue: 6
Pages: 1279 - 1283
DOI: 10.1109/LCOMM.2019.2921755
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: 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.
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