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 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) |
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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) |
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Decentralized machine learning |
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rate adaptation |
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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 |
6 |
Date of Issue |
2021-07-07 (RCS) |
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