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Paper Abstract and Keywords
Presentation 2025-03-18 14:45
Towards Adaptive IoT Networks: A Similarity-Driven Approach for Decentralized Federated Learning
Berhe Gebreegziabher Hagos, Fumiya Arai, Takao Marukame, Tetsuya Asai, Kota Ando (Hokkaido Univ.) CCS2024-62
Abstract (in Japanese) (See Japanese page) 
(in English) With the rapid evolution of autonomous Internet of Things (IoT) networks, decentralized federated learning (DFL) has been recently adopted as an effective paradigm for collaborative knowledge sharing among edge devices abandoning the role of central authority with the essence of data and privacy combined with communication resources are preserved. The absence of the central server as well as the heterogeneity of large-scale networks drift each device’s model to its local objectives, resulting in poor collaborative knowledge sharing. Apart from heavy computational and communication loads over constrained edge devices, direct model sharing over the entire network results in harmful updates due to objective dissimilarity among devices, leading to suboptimal performance. This work proposes an adaptive DFL approach towards similarity-driven IoT collaborations. We employed a fusion of Jensen Shannon Diversity and Cosine Similarity (JSD-CosNet) method to dynamically identify potential collaborators under the assumption of unstable environment and heterogeneous devices. The method ensures each agent collaborates with its most relevant IoT agents preserving efficient communication and knowledge generalization by leveraging collaborations based on task similarity. The system is found robust to the intermittent nature of IoT devices, ensuring the adaptation of new devices into the network while maintaining overall stability.
Keyword (in Japanese) (See Japanese page) 
(in English) Decentralized Federated Learning (DFL) / Central server / JSD-CosNet / Internet of Things (IoT) / Similarity-driven / Dynamic environment / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 442, CCS2024-62, pp. 41-46, March 2025.
Paper # CCS2024-62 
Date of Issue 2025-03-11 (CCS) 
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 CCS2024-62

Conference Information
Committee CCS  
Conference Date 2025-03-18 - 2025-03-19 
Place (in Japanese) (See Japanese page) 
Place (in English) RUSUTSU RESORT 
Topics (in Japanese) (See Japanese page) 
Topics (in English) CCS, etc. 
Paper Information
Registration To CCS 
Conference Code 2025-03-CCS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Towards Adaptive IoT Networks: A Similarity-Driven Approach for Decentralized Federated Learning 
Sub Title (in English)  
Keyword(1) Decentralized Federated Learning (DFL)  
Keyword(2) Central server  
Keyword(3) JSD-CosNet  
Keyword(4) Internet of Things (IoT)  
Keyword(5) Similarity-driven  
Keyword(6) Dynamic environment  
Keyword(7)  
Keyword(8)  
1st Author's Name Berhe Gebreegziabher Hagos  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Fumiya Arai  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Takao Marukame  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Tetsuya Asai  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
5th Author's Name Kota Ando  
5th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2025-03-18 14:45:00 
Presentation Time 25 minutes 
Registration for CCS 
Paper # CCS2024-62 
Volume (vol) vol.124 
Number (no) no.442 
Page pp.41-46 
#Pages 6 
Date of Issue 2025-03-11 (CCS) 

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