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Paper Abstract and Keywords
Presentation 2024-02-29 10:10
Model Shifting Method in Federated Learning Using Distillation
Hiromichi Yajima (SIT), Shota Ono (The Univ. of Tokyo), Takumi Miyoshi, Taku Yamazaki (SIT) NS2023-186
Abstract (in Japanese) (See Japanese page) 
(in English) Due to the drastic increase in the data for machine learning, distributed machine learning such as federated learning has been attracting attention to avoid the intensive load on a server. Since the process of machine learning requires a huge amount of computation, it is difficult to perform federated learning process on low-performance client devices. To solve this problem, federated learning with downsizing machine learning model by distillation has been proposed. This method can reduce the processing cost and learning time on the clients but degrades the achieved accuracy of machine learning model. This paper proposes a method that improves the accuracy of machine learning model in a short time with downsized machine learning model in the early stage of learning and then switches to general federated learning process. From the experimental results, the proposed method can rapidly improve the accuracy of machine learning model while counteracting the degradation of the final accuracy achieved.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / Federated learning / Distillation / Machine learning model / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 397, NS2023-186, pp. 86-89, Feb. 2024.
Paper # NS2023-186 
Date of Issue 2024-02-22 (NS) 
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 NS2023-186

Conference Information
Committee NS IN  
Conference Date 2024-02-29 - 2024-03-01 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2024-02-NS-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Model Shifting Method in Federated Learning Using Distillation 
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Keyword(1) Machine learning  
Keyword(2) Federated learning  
Keyword(3) Distillation  
Keyword(4) Machine learning model  
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1st Author's Name Hiromichi Yajima  
1st Author's Affiliation Shibaura Institute of Technology (SIT)
2nd Author's Name Shota Ono  
2nd Author's Affiliation The University of Tokyo (The Univ. of Tokyo)
3rd Author's Name Takumi Miyoshi  
3rd Author's Affiliation Shibaura Institute of Technology (SIT)
4th Author's Name Taku Yamazaki  
4th Author's Affiliation Shibaura Institute of Technology (SIT)
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Speaker Author-1 
Date Time 2024-02-29 10:10:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2023-186 
Volume (vol) vol.123 
Number (no) no.397 
Page pp.86-89 
#Pages 4 
Date of Issue 2024-02-22 (NS) 

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