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| Paper Abstract and Keywords | ||
| Presentation | 2023-05-12 13:55 [Invited Talk] Federated Learning-Inspired Gaussian Process Regression: Low Latency Design and Its Application to Radio Map Construction Koya Sato (UEC)  SR2023-20 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Gaussian process regression (GPR) is a non-parametric method that optimizes regression analysis for Gaussian process data. There has been a wide range of applications, such as environmental monitoring and robotics. However, GPR has drawbacks regarding computational complexity and communication cost for collecting sensing data; it will be significant in the massive-dataset analysis. This presentation gives recent progress in distributed GPR over wireless networks toward low latency and accurate regression analysis. It is also shown that the distributed GPR can be applied for radio map construction tasks, an application of GPR in wireless communications. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Gaussian process regression / distributed machine learning / over-the-air computation / radio map / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 19, SR2023-20, pp. 91-91, May 2023. | |
| Paper # | SR2023-20 | |
| Date of Issue | 2023-05-04 (SR) | |
| 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 | SR2023-20 |
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| Conference Information | |
| Committee | SR |
| Conference Date | 2023-05-11 - 2023-05-12 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Center of lifelong learning Kiran (Higashi Muroran) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Software Defined Radio, Cognitive Radio, Spectrum Sharing, Machine Learning, etc. |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2023-05-SR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Federated Learning-Inspired Gaussian Process Regression: Low Latency Design and Its Application to Radio Map Construction |
| Sub Title (in English) | |
| Keyword(1) | Gaussian process regression |
| Keyword(2) | distributed machine learning |
| Keyword(3) | over-the-air computation |
| Keyword(4) | radio map |
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| 1st Author's Name | Koya Sato |
| 1st Author's Affiliation | The University of Electro-Communications (UEC) |
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| Speaker | Author-1 |
| Date Time | 2023-05-12 13:55:00 |
| Presentation Time | 50 minutes |
| Registration for | SR |
| Paper # | SR2023-20 |
| Volume (vol) | vol.123 |
| Number (no) | no.19 |
| Page | p.91 |
| #Pages | 1 |
| Date of Issue | 2023-05-04 (SR) |
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