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Paper Abstract and Keywords
Presentation 2022-10-05 17:05
[Invited Lecture] Reducing Device Processing Load and Communication Overhead by Distillation in Federated Learning
Hiromichi Yajima, Takumi Miyoshi, Taku Yamazaki (Shibaura Inst. of Tech.), Shota Ono (The Univ. of Tokyo) NS2022-87
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, machine learning has been used in many cases to discover the rules or to predict future results from a large amount of data. Although current machine learning commonly aggregates data centrally on a server, the drastic increase in the data for machine learning makes it difficult to calculate on a single server. Therefore, distributed machine learning such as federated learning has been attracting attention to avoid the concentrated load on the server. Nevertheless, since the process of machine learning requires a huge amount of computation, it is difficult to perform federated learning process on small devices. This paper proposes a method to reduce device processing load and communication overhead by distillation in federated learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / Federated learning / Distillation / Processing load / Communication overhead / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 198, NS2022-87, pp. 29-32, Oct. 2022.
Paper # NS2022-87 
Date of Issue 2022-09-28 (NS) 
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 NS2022-87

Conference Information
Committee NS  
Conference Date 2022-10-05 - 2022-10-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaidou University + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network architecture (5G, Local 5G, Beyond5G, Mobile networks, Ad-hoc and sensor networks, Overlay and P2P networks, Programmable networks, SDN/NFV, IoT, Network slicing), Next generation packet transport (High speed Ethernet, IP over WDM, Multi-service package technology, MPLS), Grid, etc. 
Paper Information
Registration To NS 
Conference Code 2022-10-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Reducing Device Processing Load and Communication Overhead by Distillation in Federated Learning 
Sub Title (in English)  
Keyword(1) Machine learning  
Keyword(2) Federated learning  
Keyword(3) Distillation  
Keyword(4) Processing load  
Keyword(5) Communication overhead  
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1st Author's Name Hiromichi Yajima  
1st Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. of Tech.)
2nd Author's Name Takumi Miyoshi  
2nd Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. of Tech.)
3rd Author's Name Taku Yamazaki  
3rd Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. of Tech.)
4th Author's Name Shota Ono  
4th Author's Affiliation The University of Tokyo (The Univ. of Tokyo)
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Speaker Author-1 
Date Time 2022-10-05 17:05:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2022-87 
Volume (vol) vol.122 
Number (no) no.198 
Page pp.29-32 
#Pages 4 
Date of Issue 2022-09-28 (NS) 

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