Blockchained on-device federated learning
|Author:||Kim, Hyesung1; Park, Jihong2; Bennis, Mehdi2;|
1School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
2Centre for Wireless Communications, University of Oulu, 4500 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 2.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019120946269
Institute of Electrical and Electronics Engineers,
|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.
IEEE communications letters
|Pages:||1279 - 1283|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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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