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Paper Abstract and Keywords
Presentation 2025-03-07 13:25
Feasibility Study of Federated Learning Systems Using IOWN APN
Sakurako Tamura, Takumi Fukami, Yusuke Yamasaki, Naho Isogai (NTT) IN2024-123
Abstract (in Japanese) (See Japanese page) 
(in English) Federated learning has attracted attention as a technology that enables AI model training without sharing data by exchanging and integrating only locally trained models between clients and a central server. However, during the learning process, continuous communication between clients and the server can become a bottleneck, especially in Large Language Models with significant model sizes.
In this paper, we investigate the feasibility of federated learning using NTT's optical communication network environment, IOWN APN, which provides high bandwidth and low latency. We evaluate the communication overhead associated with the application or non-application of a secure optical transport network, which enhances the security of IOWN APN.
Furthermore, we measure the processing time for each step of federated learning in both the IOWN APN environment and a standard internet environment, analyzing how the communication environment between clients and the server affects the overall processing time of federated learning. The results of this study provide insights into the effectiveness of high-speed, low-latency networks for federated learning, particularly in mitigating communication bottlenecks for Large Language Models.
Keyword (in Japanese) (See Japanese page) 
(in English) Federated Learning / IOWN APN / LLM / Communication overhead / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 420, IN2024-123, pp. 272-277, March 2025.
Paper # IN2024-123 
Date of Issue 2025-02-27 (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)
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Conference Information
Committee IN NS  
Conference Date 2025-03-06 - 2025-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To IN 
Conference Code 2025-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Feasibility Study of Federated Learning Systems Using IOWN APN 
Sub Title (in English)  
Keyword(1) Federated Learning  
Keyword(2) IOWN APN  
Keyword(3) LLM  
Keyword(4) Communication overhead  
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1st Author's Name Sakurako Tamura  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Takumi Fukami  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Yusuke Yamasaki  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Naho Isogai  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2025-03-07 13:25:00 
Presentation Time 25 minutes 
Registration for IN 
Paper # IN2024-123 
Volume (vol) vol.124 
Number (no) no.420 
Page pp.272-277 
#Pages 6 
Date of Issue 2025-02-27 (IN) 

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