Paper Abstract and Keywords |
Presentation |
2022-01-21 15:20
[Short Paper]
Asynchronous Gradient-Boosted Decision Trees for Distributed Sensing Devices Yui Yamashita, Akihito Taya, Yoshito Tobe (Aoyama Gakuin Univ.) SeMI2021-64 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recently, wearable devices that install multiple sensors have been widely used. Although sensor data from these devices are used to train machine learning models, to improve performance of the model of machine learning requires significant amount of data. Federated learning (FL) is proposed as a cooperative distributed machine learning without sharing personal data, but it still requires a server to manage devices and aggregate models in a central way. This paper proposes a serverless collaborative learning algorithm to reduce the maintenance cost of the server and prevent only good models are concentrated in a few companies. The proposed method adopts gradient boosting decision tree (GBDT) so that devices with a limited computation resources can join in the collaborative learning. Moreover, the devices can asynchronously share the models in collaborative learning to improving learning speed considering a difference in calculation speed among the devices. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Internet of Things / Distributed learning / Gradient boosting decision tree / Federated learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 333, SeMI2021-64, pp. 45-47, Jan. 2022. |
Paper # |
SeMI2021-64 |
Date of Issue |
2022-01-13 (SeMI) |
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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SeMI2021-64 |
Conference Information |
Committee |
SeMI |
Conference Date |
2022-01-20 - 2022-01-21 |
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Paper Information |
Registration To |
SeMI |
Conference Code |
2022-01-SeMI |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
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(See Japanese page) |
Title (in English) |
Asynchronous Gradient-Boosted Decision Trees for Distributed Sensing Devices |
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Internet of Things |
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Distributed learning |
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Gradient boosting decision tree |
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Federated learning |
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1st Author's Name |
Yui Yamashita |
1st Author's Affiliation |
Aoyama Gakuin University (Aoyama Gakuin Univ.) |
2nd Author's Name |
Akihito Taya |
2nd Author's Affiliation |
Aoyama Gakuin University (Aoyama Gakuin Univ.) |
3rd Author's Name |
Yoshito Tobe |
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Aoyama Gakuin University (Aoyama Gakuin Univ.) |
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Speaker |
Author-1 |
Date Time |
2022-01-21 15:20:00 |
Presentation Time |
10 minutes |
Registration for |
SeMI |
Paper # |
SeMI2021-64 |
Volume (vol) |
vol.121 |
Number (no) |
no.333 |
Page |
pp.45-47 |
#Pages |
3 |
Date of Issue |
2022-01-13 (SeMI) |
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