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) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2019120946269 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2020
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Publish Date: | 2019-12-09 |
Description: |
AbstractBy 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. see all
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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 |
OADOI: | https://oadoi.org/10.1109/LCOMM.2019.2921755 |
Type of Publication: |
A1 Journal article – refereed |
Field of Science: |
213 Electronic, automation and communications engineering, electronics |
Subjects: | |
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. |
Copyright information: |
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