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Paper Abstract and Keywords
Presentation 2024-02-29 17:20
An Enhanced Privacy-Preserving Scheme for Federated Learning of Vision Transformer without Model Performance Degradation
Rei Aso, Sayaka Shiota, Hitoshi Kiya (Tokyo Metropolitan Univ.) EA2023-80 SIP2023-127 SP2023-62
Abstract (in Japanese) (See Japanese page) 
(in English) Federated learning is a learning method for training models over multiple participants without directly sharing their raw data, and it is expected to be a privacy protection method for training data. In contrast, attack methods have been studied to restore learning data from model information shared with clients, so enhanced security against attacks has become an urgent problem. Accordingly, we propose a novel framework of privacy-preserving federated learning in which model information of each client is encrypted with a random sequence, and a cancelable noise is added to the encrypted information. The proposed framework allows us not only to avoid performance degradation caused by enhancing security, which conventional methods such as differential privacy cannot, but to also be robust against attacks even when encryption keys are leaked. In experiments, we verify the effectiveness of the proposed method on the CIFAR-10 dataset in terms of classification accuracy and robustness against attacks.
Keyword (in Japanese) (See Japanese page) 
(in English) Federated Learning / Vision Transformer / Praivacy-preserving / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 402, SIP2023-127, pp. 115-120, Feb. 2024.
Paper # SIP2023-127 
Date of Issue 2024-02-22 (EA, SIP, SP) 
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 EA2023-80 SIP2023-127 SP2023-62

Conference Information
Committee SIP SP EA IPSJ-SLP  
Conference Date 2024-02-29 - 2024-03-01 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2024-02-SIP-SP-EA-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Enhanced Privacy-Preserving Scheme for Federated Learning of Vision Transformer without Model Performance Degradation 
Sub Title (in English)  
Keyword(1) Federated Learning  
Keyword(2) Vision Transformer  
Keyword(3) Praivacy-preserving  
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1st Author's Name Rei Aso  
1st Author's Affiliation Tokyo Metropolitan University (Tokyo Metropolitan Univ.)
2nd Author's Name Sayaka Shiota  
2nd Author's Affiliation Tokyo Metropolitan University (Tokyo Metropolitan Univ.)
3rd Author's Name Hitoshi Kiya  
3rd Author's Affiliation Tokyo Metropolitan University (Tokyo Metropolitan Univ.)
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Speaker Author-1 
Date Time 2024-02-29 17:20:00 
Presentation Time 20 minutes 
Registration for SIP 
Paper # EA2023-80, SIP2023-127, SP2023-62 
Volume (vol) vol.123 
Number (no) no.401(EA), no.402(SIP), no.403(SP) 
Page pp.115-120 
#Pages 6 
Date of Issue 2024-02-22 (EA, SIP, SP) 

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