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Paper Abstract and Keywords
Presentation 2026-03-04 10:20
Bandwidth Assignment and Routing for Efficient Collection of Correlated Data in Federated Learning
Tomoya Sugiue, Kazuhiko Kinoshita, Alberto Gallegos Ramonet (Tokushima Univ) NS2025-221
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, federated learning has been attracting attention as a method for obtaining sophisticated models efficiently by sharing models built locally on multiple servers. In this thesis, we assume the collection of bulk data grouped by correlation. In this assumption, if data transfer to a specific server is delayed, the overall time required to complete data collection increases. Furthermore, if the proportion of a specific group in the server's collected data is skewed, it may degrade the accuracy of the learning model. Therefore, in this research, we propose routing and bandwidth assigment methods for shortening the data collection completion time, under the constraint that
routers transfer data to servers in the same proportion as the constrained data amount for each group. Specifically, the flow is determined by gradually applying a preferred destination server selection based on the server's remaining required data volume, a load-balancing path selection to avoid congestion using the k-th shortest path algorithm, and proportional bandwidth assigment to synchronize the data collection rate of each server. Finally, we evaluate the performance of the proposed method by simulation experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) federated learning / routing / bandwidth assigment / correlated data / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 385, NS2025-221, pp. 1-6, March 2026.
Paper # NS2025-221 
Date of Issue 2026-02-25 (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 NS2025-221

Conference Information
Committee IN NS  
Conference Date 2026-03-04 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa-Ken Shichoson Jichi Kaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2026-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Bandwidth Assignment and Routing for Efficient Collection of Correlated Data in Federated Learning 
Sub Title (in English)  
Keyword(1) federated learning  
Keyword(2) routing  
Keyword(3) bandwidth assigment  
Keyword(4) correlated data  
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1st Author's Name Tomoya Sugiue  
1st Author's Affiliation Tokushima University (Tokushima Univ)
2nd Author's Name Kazuhiko Kinoshita  
2nd Author's Affiliation Tokushima University (Tokushima Univ)
3rd Author's Name Alberto Gallegos Ramonet  
3rd Author's Affiliation Tokushima University (Tokushima Univ)
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Speaker Author-1 
Date Time 2026-03-04 10:20:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2025-221 
Volume (vol) vol.125 
Number (no) no.385 
Page pp.1-6 
#Pages 6 
Date of Issue 2026-02-25 (NS) 

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