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Paper Abstract and Keywords
Presentation 2024-02-29 09:45
Communication cost and performance evaluation of each learning method in Federated learning with LLM
Takumi Fukami, Yusuke Yamasaki, Iifan Tyou (NTT) IN2023-66
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, a large amount of diverse data have been generated by various devices and organisations, and there has been a growing movement to utilise these data. In general, data collection is indispensable for data utilisation such as learning and prediction by AI. However, if the data is confidential information, the data should not be collected. One solution to this problem is federated learning, which can learn high-performance AI by sharing only the AI models learned by each data holder without collecting their data. It is well known that the larger the models to be learned, the higher the communication cost, but it is not clear how much communication is actually required in operation. In this paper, we report the communication cost and performance of learning the large language model, which requires a large amount of communication cost, using each learning method of federated learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Federated Learning / Natural language processing / LLM / Communication cost / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 398, IN2023-66, pp. 7-12, Feb. 2024.
Paper # IN2023-66 
Date of Issue 2024-02-22 (IN) 
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 IN2023-66

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 IN 
Conference Code 2024-02-NS-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Communication cost and performance evaluation of each learning method in Federated learning with LLM 
Sub Title (in English)  
Keyword(1) Federated Learning  
Keyword(2) Natural language processing  
Keyword(3) LLM  
Keyword(4) Communication cost  
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1st Author's Name Takumi Fukami  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Yusuke Yamasaki  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Iifan Tyou  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2024-02-29 09:45:00 
Presentation Time 25 minutes 
Registration for IN 
Paper # IN2023-66 
Volume (vol) vol.123 
Number (no) no.398 
Page pp.7-12 
#Pages 6 
Date of Issue 2024-02-22 (IN) 

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