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Paper Abstract and Keywords
Presentation 2024-03-04 11:10
Towards Client-aware Clustering Federated Learning based on Representations of Local Models
Tatsuya Kaneko, Shinya Takamaeda-Yamazaki (Tokyo Univ.) IBISML2023-49
Abstract (in Japanese) (See Japanese page) 
(in English) In the current era of rapidly expanding machine learning, there has been growing concerns and awareness of data privacy used in learning processes. Federated Learning (FL) is one of the distributed learning methods that is attracting significant attention. It enables knowledge sharing while maintaining data confidentiality by aggregating models trained on various devices.One of the challenges faced by FL is the heterogeneity of data across client devices, which can potentially degrade performance when models are shared. To address this issue, clustering FL (CFL), which assigns each client to an appropriate cluster, has been proposed. However, conventional CFL methods have limitations in their assignment approaches. Despite resource constraints, clustering computations are being performed on the client.We propose a novel client-aware CFL method, which is based on the feature representation of aggregated models using fractal datasets and Fr'{e}chet Inception Distance. In the experiments, we show that our proposed method can achieve performance equivalent to conventional methods, while reducing the clustering overhead for clients to $1/k$ (where $k$ is the number of clusters).
Keyword (in Japanese) (See Japanese page) 
(in English) Machine Learning / Federated Learning (FL) / Personalized FL / Clustering FL / Edge-AI / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 410, IBISML2023-49, pp. 65-70, March 2024.
Paper # IBISML2023-49 
Date of Issue 2024-02-25 (IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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 IBISML2023-49

Conference Information
Committee PRMU IBISML IPSJ-CVIM  
Conference Date 2024-03-03 - 2024-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima Univ. Higashi-Hiroshima campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2024-03-PRMU-IBISML-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Towards Client-aware Clustering Federated Learning based on Representations of Local Models 
Sub Title (in English)  
Keyword(1) Machine Learning  
Keyword(2) Federated Learning (FL)  
Keyword(3) Personalized FL  
Keyword(4) Clustering FL  
Keyword(5) Edge-AI  
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1st Author's Name Tatsuya Kaneko  
1st Author's Affiliation The University of Tokyo (Tokyo Univ.)
2nd Author's Name Shinya Takamaeda-Yamazaki  
2nd Author's Affiliation The University of Tokyo (Tokyo Univ.)
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Speaker Author-1 
Date Time 2024-03-04 11:10:00 
Presentation Time 15 minutes 
Registration for IBISML 
Paper # IBISML2023-49 
Volume (vol) vol.123 
Number (no) no.410 
Page pp.65-70 
#Pages 6 
Date of Issue 2024-02-25 (IBISML) 

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