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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
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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)
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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 
Sub Title (in English)  
Keyword(1) distributed learning  
Keyword(2) neural network  
Keyword(3) 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  
3rd Author's Affiliation 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
Date of Issue 2021-03-08 (NLP) 


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