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Paper Abstract and Keywords
Presentation 2023-05-12 10:25
A Study on Adaptive Client/Miner Selection for Fast and Accurate Blockchain-Decentralized Federated Learning
Yuta Tomimasu, Koya Sato (UEC) SR2023-17
Abstract (in Japanese) (See Japanese page) 
(in English) Decentralized federative learning with blockchain is a learning method in which the model in federated learning is managed on a blockchain. Although blockchain is expected to improve the security of model sharing and realize reward management, mining increases round time. Additionally, if each client's data distribution follows non independent and identically distributed (Non-IID) condition, the learning accuracy could be degraded. In this study, we investigate the application of a client selection algorithm based on the estimation of label distributions to decentralized federative learning with blockchain. Client selection allows parallel processing of learning and mining on the network, reducing rounding time. In addition, by using a client selection algorithm based on the estimation of the label distribution, the accuracy degradation caused by Non-IID is suppressed. We show that the proposed method can achieve faster and more accurate learning by comparing with frameworks that do not use it.
Keyword (in Japanese) (See Japanese page) 
(in English) Decentralized Federated Learning / Blockchain / Client selection / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 19, SR2023-17, pp. 83-88, May 2023.
Paper # SR2023-17 
Date of Issue 2023-05-04 (SR) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SR2023-17

Conference Information
Committee SR  
Conference Date 2023-05-11 - 2023-05-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Center of lifelong learning Kiran (Higashi Muroran) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Software Defined Radio, Cognitive Radio, Spectrum Sharing, Machine Learning, etc. 
Paper Information
Registration To SR 
Conference Code 2023-05-SR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Adaptive Client/Miner Selection for Fast and Accurate Blockchain-Decentralized Federated Learning 
Sub Title (in English)  
Keyword(1) Decentralized Federated Learning  
Keyword(2) Blockchain  
Keyword(3) Client selection  
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1st Author's Name Yuta Tomimasu  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Koya Sato  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2023-05-12 10:25:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2023-17 
Volume (vol) vol.123 
Number (no) no.19 
Page pp.83-88 
#Pages 6 
Date of Issue 2023-05-04 (SR) 

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