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Paper Abstract and Keywords
Presentation 2025-11-06 13:40
On Training Latency-aware Resource Allocation in Multi-hop Split Learning
Akihiro Inoue, Takanori Hara, Shoji Kasahara (NAIST) NS2025-137
Abstract (in Japanese) (See Japanese page) 
(in English) Split Learning (SL) is a promising technology that simultaneously reduces computational workload on clients and protects data privacy, partitioning a global model into two sub-models placed on a client and a server. However, with the increasing scale of recent AI models, the smashed data produced by sub-models becomes massive, leading to increased memory consumption on computing nodes and bandwidth congestion. To address this issue, multi-hop SL (MSL) has been proposed, partitioning the model into multiple sub-models placed on relay nodes over a communication network. In this paper, we formulate an optimization problem that jointly determines model splitting and wireless network resource allocation in MSL, including subchannel selection and transmit power control, as a Mixed Integer Nonlinear Programming (MINLP) problem with the objective of minimizing training latency. Through numerical experiments, we quantitatively evaluate how model splitting, subchannel allocation, and transmit power affect the training latency. We reveal fundamental characteristics that yield design insights for effective MSL system configurations.
Keyword (in Japanese) (See Japanese page) 
(in English) Split Learning / Multi-hop Split Learning / Model splitting / Transmit Power Control / Training Latency / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 225, NS2025-137, pp. 37-42, Nov. 2025.
Paper # NS2025-137 
Date of Issue 2025-10-30 (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 NS2025-137

Conference Information
Committee ICM NS NV  
Conference Date 2025-11-06 - 2025-11-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Daisan-Hakata-Kaisei Bldg. + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network quality, Network measurement/management, Network virtualization, Network service, Blockchain, Security, Network intelligence/AI, etc. 
Paper Information
Registration To NS 
Conference Code 2025-11-ICM-NS-NV 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) On Training Latency-aware Resource Allocation in Multi-hop Split Learning 
Sub Title (in English)  
Keyword(1) Split Learning  
Keyword(2) Multi-hop Split Learning  
Keyword(3) Model splitting  
Keyword(4) Transmit Power Control  
Keyword(5) Training Latency  
Keyword(6)  
Keyword(7)  
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1st Author's Name Akihiro Inoue  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Takanori Hara  
2nd Author's Affiliation Nara Institute of Science and Technology (NAIST)
3rd Author's Name Shoji Kasahara  
3rd Author's Affiliation Nara Institute of Science and Technology (NAIST)
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Speaker Author-1 
Date Time 2025-11-06 13:40:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2025-137 
Volume (vol) vol.125 
Number (no) no.225 
Page pp.37-42 
#Pages 6 
Date of Issue 2025-10-30 (NS) 

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