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Paper Abstract and Keywords
Presentation 2024-05-10 10:30
Federated Learning Algorithms based on Decentralized Spanning Tree Generation and Step-by-Step Consensus
Yuki Mori, Tatsuya Kayatani, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2024-11
Abstract (in Japanese) (See Japanese page) 
(in English) A large amount of high-quality data is necessary to improve the learning accuracy of neural networks. However, there are many cases where data cannot be aggregated for privacy protection and other reasons. As a framework to overcome this difficulty, the federated learning is attracting attention. In the federated learning, neural network models are deployed at each location, and each model is trained by repeating the process of updating parameter values using its own data and making a consensus on the parameter values with other models. Recently, a method for models to make a consensus on the parameter values in a decentralized and adaptive manner was proposed, in which a spanning tree is generated in a decentralized manner and the parameter values are aggregated and distributed along the tree. However, this method requires the transmission and reception of all the parameter values, which may cause network bandwidth congestion and a long time for reaching consensus. To solve these problems, we propose in this report a new federated learning algorithms based on decentralized spanning tree construction and step-by-step consensus, and verify their effectiveness experimentally. The proposed algorithms are characterized by the fact that a consensus on the parameter values is made at each layer of the neural network, which enables learning with low communication costs.
Keyword (in Japanese) (See Japanese page) 
(in English) neural networks / federated learning / consensus / spanning tree / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 13, NLP2024-11, pp. 52-57, May 2024.
Paper # NLP2024-11 
Date of Issue 2024-05-02 (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)
Download PDF NLP2024-11

Conference Information
Committee NLP  
Conference Date 2024-05-09 - 2024-05-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Kagawa Prefecture Social Welfare Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear Problems, etc. 
Paper Information
Registration To NLP 
Conference Code 2024-05-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Federated Learning Algorithms based on Decentralized Spanning Tree Generation and Step-by-Step Consensus 
Sub Title (in English)  
Keyword(1) neural networks  
Keyword(2) federated learning  
Keyword(3) consensus  
Keyword(4) spanning tree  
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1st Author's Name Yuki Mori  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Tatsuya Kayatani  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Tsuyoshi Migita  
3rd Author's Affiliation Okayama University (Okayama Univ.)
4th Author's Name Norikazu Takahashi  
4th Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-1 
Date Time 2024-05-10 10:30:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2024-11 
Volume (vol) vol.124 
Number (no) no.13 
Page pp.52-57 
#Pages 6 
Date of Issue 2024-05-02 (NLP) 

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