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Paper Abstract and Keywords
Presentation 2026-07-23 09:00
[Poster Presentation] Comparative Evaluation of Training Stability for Federated Learning-Based Indoor Localization under Non-IID Conditions
Shuya Ishikawa, Takuya Asaka (TMU) CQ2026-43
Abstract (in Japanese) (See Japanese page) 
(in English) Fingerprint (FP) based indoor positioning requires aggregating users' location data at a central server, raising privacy concerns. Federated learning (FL) avoids this, but its training becomes unstable when client data distributions are non-independent and identically distributed (Non-IID). Although several Non-IID-oriented FL methods exist, none have been systematically compared for indoor positioning under a unified setting. Using a public Wi-Fi FP dataset, we evaluate six methods, FedAvg and five Non-IID-oriented variants, in terms of positioning accuracy, convergence speed, and stability. Adaptive optimization methods generally outperform FedAvg; FedAdam attains the best accuracy and fastest convergence but exhibits less stable learning dynamics, whereas FedYogi achieves the smallest positioning error with good convergence, offering the best overall balance.
Keyword (in Japanese) (See Japanese page) 
(in English) Indoor localization / Federated learning / Non-IID / Fingerprinting / Privacy preservation / Deep learning / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 121, CQ2026-43, pp. 156-161, July 2026.
Paper # CQ2026-43 
Date of Issue 2026-07-14 (CQ) 
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 CQ2026-43

Conference Information
Committee CQ  
Conference Date 2026-07-21 - 2026-07-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Eef Jouhou Plaza 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CQ 
Conference Code 2026-07-CQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Comparative Evaluation of Training Stability for Federated Learning-Based Indoor Localization under Non-IID Conditions 
Sub Title (in English)  
Keyword(1) Indoor localization  
Keyword(2) Federated learning  
Keyword(3) Non-IID  
Keyword(4) Fingerprinting  
Keyword(5) Privacy preservation  
Keyword(6) Deep learning  
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Keyword(8)  
1st Author's Name Shuya Ishikawa  
1st Author's Affiliation Tokyo Metropolitan University (TMU)
2nd Author's Name Takuya Asaka  
2nd Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2026-07-23 09:00:00 
Presentation Time 60 minutes 
Registration for CQ 
Paper # CQ2026-43 
Volume (vol) vol.126 
Number (no) no.121 
Page pp.156-161 
#Pages 6 
Date of Issue 2026-07-14 (CQ) 

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