Paper Abstract and Keywords |
Presentation |
2021-03-15 13:00
Proposal of Novel Distributed Learning Algorithms for Multi-Neural Networks Kazuaki Harada, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2020-58 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
A method for multiple neural networks (NNs) with the same structure to learn multiple sets of training data collected at different institutions or places in a distributed manner without aggregating the data has recently been proposed. In this method, each NN iteratively updates the parameter values in the direction obtained by composing the direction of the steepest descent of the loss function for its own training data and the weighted average of the parameter values of its neighboring NNs. Under some conditions on the structure of the graph represening the communication between NNs and the weights for computing the average, it has been proved that the parameter values of all NNs converge to the same stationary point of the loss function for all training data. However, when the training data sets differ from NN to NN and the graph is not dense, it leads to decrease in learning accuracy. In this report, we propose a novel distributed learning method that prevents the degredation of learning accuracy even in such cases, and evaluate its effectiveness experimentally. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
distributed learning / neural network / multi-agent network / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 430, NLP2020-58, pp. 17-22, March 2021. |
Paper # |
NLP2020-58 |
Date of Issue |
2021-03-08 (NLP) |
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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NLP2020-58 |
Conference Information |
Committee |
NLP MSS |
Conference Date |
2021-03-15 - 2021-03-16 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
MSS, NLP, Work In Progress (MSS only), and etc. |
Paper Information |
Registration To |
NLP |
Conference Code |
2021-03-NLP-MSS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Proposal of Novel Distributed Learning Algorithms for Multi-Neural Networks |
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distributed learning |
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neural network |
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multi-agent network |
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1st Author's Name |
Kazuaki Harada |
1st Author's Affiliation |
Okayama University (Okayama Univ.) |
2nd Author's Name |
Tsuyoshi Migita |
2nd Author's Affiliation |
Okayama University (Okayama Univ.) |
3rd Author's Name |
Norikazu Takahashi |
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Okayama University (Okayama Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-03-15 13:00:00 |
Presentation Time |
25 minutes |
Registration for |
NLP |
Paper # |
NLP2020-58 |
Volume (vol) |
vol.120 |
Number (no) |
no.430 |
Page |
pp.17-22 |
#Pages |
6 |
Date of Issue |
2021-03-08 (NLP) |
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