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Paper Abstract and Keywords
Presentation 2026-03-05 13:25
A Decentralized Federated Learning Framework for Heterogeneous IoT Networks
Yunge Xu, Tomoaki Ohtsuki (Keio) RCS2025-278
Abstract (in Japanese) (See Japanese page) 
(in English) We study decentralized federated learning (DFL) in Internet-of-Things (IoT) networks, where clients collaboratively train models without a central server and without sharing raw data. In real-world scenarios, heterogeneity poses a major challenge. First, different clients often require customized models tailored to diverse tasks. Second, client drift can significantly slow convergence. To address these issues, we propose a knowledge-distillation-based DFL method grounded in deep mutual learning (DML). Each participant maintains two models: a proxy model and a local model. During local training, the two models perform mutual distillation without constructing any public dataset. The proposed approach supports model heterogeneity, allowing each client to adopt an arbitrary architecture for its local model. Information is exchanged directly and efficiently among participants via shared proxy models. Experimental results demonstrate that our method achieves substantial improvements in convergence speed, accuracy, and communication cost.
Keyword (in Japanese) (See Japanese page) 
(in English) decentralized federated learning / IoT networks / knowledge distillation / deep mutual learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 387, RCS2025-278, pp. 110-115, March 2026.
Paper # RCS2025-278 
Date of Issue 2026-02-25 (RCS) 
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 RCS2025-278

Conference Information
Committee RCS SR SRW  
Conference Date 2026-03-04 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo University of Science 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To RCS 
Conference Code 2026-03-RCS-SR-SRW 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Decentralized Federated Learning Framework for Heterogeneous IoT Networks 
Sub Title (in English)  
Keyword(1) decentralized federated learning  
Keyword(2) IoT networks  
Keyword(3) knowledge distillation  
Keyword(4) deep mutual learning  
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1st Author's Name Yunge Xu  
1st Author's Affiliation Keio University (Keio)
2nd Author's Name Tomoaki Ohtsuki  
2nd Author's Affiliation Keio University (Keio)
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Speaker Author-1 
Date Time 2026-03-05 13:25:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2025-278 
Volume (vol) vol.125 
Number (no) no.387 
Page pp.110-115 
#Pages 6 
Date of Issue 2026-02-25 (RCS) 

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