Abstract
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 language | English |
|---|---|
| Article number | 8733825 |
| Pages (from-to) | 1279-1283 |
| Number of pages | 5 |
| Journal | IEEE Communications Letters |
| Volume | 24 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 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: hskim@ramo.yonsei.ac.kr; slkim@ramo.yonsei.ac.kr).
Publisher Copyright:
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All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Computer Science Applications
- Electrical and Electronic Engineering