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Paper Abstract and Keywords
Presentation 2023-03-03 09:30
[Short Paper] Performance Evaluation on Split Learning Assisted Multi-UAV System for Image Classification Task
Sun Tingkai, Wang Xiaoyan (Ibaraki Univ.), Masahiro Umehira (Nanzan Univ.) SR2022-93
Abstract (in Japanese) (See Japanese page) 
(in English) Due to its ease of deployment and high mobility, unmanned aerial vehicles (UAVs) have gained popularity for a variety of applications. Coordinating the actions of several UAVs, however, can be difficult when completing high-level, complicated tasks like search and rescue missions, target surveillance, and information dissemination.
To address this issue, distributed learning methods such as federated learning (FL) and split learning (SL) have been proposed. FL involves building a joint model by aggregating models trained on each UAV's local data, while SL involves splitting the model between the UAVs and a central server, with both parties collaborating to train the entire network. In this paper, investigations were made on the application of split learning (SL) in a multi-UAV system for image classification in scenarios including area exploration. A server was used to coordinate multiple UAVs, with each UAV using a local model trained on images gathered by its on-board camera to perform classification tasks. Local updates from all UAVs were communicated to the server, which then performed a global update and transmitted the results back to the UAVs. The performance of the proposed system was evaluated using an aerial perspective geographic dataset, and the effectiveness of SL compared to federated learning (FL) was discussed. It was found that SL significantly reduces local computation compared to FL, leading to faster learning times, and is particularly effective with unbalanced data. It also requires less data during the initial phases of training and has a faster convergence speed compared to centralized learning.
Keyword (in Japanese) (See Japanese page) 
(in English) distributed machine learning / Unmanned aerial vehicles / convolutional neural network / split learning / federated learning / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 400, SR2022-93, pp. 44-46, March 2023.
Paper # SR2022-93 
Date of Issue 2023-02-22 (SR) 
ISSN Online edition: ISSN 2432-6380
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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)
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Conference Information
Committee RCS SR SRW  
Conference Date 2023-03-01 - 2023-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo Institute of Technology, and Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To SR 
Conference Code 2023-03-RCS-SR-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Performance Evaluation on Split Learning Assisted Multi-UAV System for Image Classification Task 
Sub Title (in English)  
Keyword(1) distributed machine learning  
Keyword(2) Unmanned aerial vehicles  
Keyword(3) convolutional neural network  
Keyword(4) split learning  
Keyword(5) federated learning  
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1st Author's Name Sun Tingkai  
1st Author's Affiliation Ibaraki University (Ibaraki Univ.)
2nd Author's Name Wang Xiaoyan  
2nd Author's Affiliation Ibaraki University (Ibaraki Univ.)
3rd Author's Name Masahiro Umehira  
3rd Author's Affiliation Nanzan University (Nanzan Univ.)
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Speaker Author-1 
Date Time 2023-03-03 09:30:00 
Presentation Time 15 minutes 
Registration for SR 
Paper # SR2022-93 
Volume (vol) vol.122 
Number (no) no.400 
Page pp.44-46 
#Pages
Date of Issue 2023-02-22 (SR) 


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