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Paper Abstract and Keywords
Presentation 2021-07-16 09:25
Improving the Runtime Performance of Decentralized Machine Learning on Wireless Channels via Rate Adaptation
Koya Sato (Tokyo Univ. of Science), Daisuke Sugimura (Tsuda Univ.) RCS2021-94
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a communication strategy for improving the runtime of decentralized machine learning over wireless networks. An iteration of local training and sharing the trained parameters can realize the decentralized machine learning; however, in the wireless channel, communication time tends to be a bottleneck for the runtime performance owing to path loss and multipath fading. To deal with this problem, we focus on the tradeoff between communication time and training accuracy, which is raised by adjusting the transmission rate. We formulate the rate adaptation as a minimization problem for the communication time under the constraint for the network density. Numerical results demonstrate that the rate adaptation aids in realizing the fast decentralized machine learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Decentralized machine learning / rate adaptation / network topology / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 103, RCS2021-94, pp. 80-85, July 2021.
Paper # RCS2021-94 
Date of Issue 2021-07-07 (RCS) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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 RCS2021-94

Conference Information
Committee RCS SR NS SeMI RCC  
Conference Date 2021-07-14 - 2021-07-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Communication and Network Technology of the AI Age, M2M (Machine-to-Machine),D2D (Device-to-Device),IoT(Internet of Things), etc 
Paper Information
Registration To RCS 
Conference Code 2021-07-RCS-SR-NS-SeMI-RCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improving the Runtime Performance of Decentralized Machine Learning on Wireless Channels via Rate Adaptation 
Sub Title (in English)  
Keyword(1) Decentralized machine learning  
Keyword(2) rate adaptation  
Keyword(3) network topology  
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1st Author's Name Koya Sato  
1st Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
2nd Author's Name Daisuke Sugimura  
2nd Author's Affiliation Tsuda University (Tsuda Univ.)
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Speaker Author-1 
Date Time 2021-07-16 09:25:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2021-94 
Volume (vol) vol.121 
Number (no) no.103 
Page pp.80-85 
#Pages
Date of Issue 2021-07-07 (RCS) 


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