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| Presentation | 2021-11-05 11:15 Throughput Prediction by Radio Environment Correlation Recognition Using Crowd Sensing and Federated Learning Satoshi Nakaniida, Takeo Fujii (UEC)  SR2021-53 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We propose an approach using federated learning for predicting Wi-Fi and LTE transmission control protocol (TCP) throughput to reduce the delay between the output of prediction results and the problem of security risks by sharing the datasets, which is a problem with conventional machine learning methods. In this study, we constructed a machine learning model, implemented the federated learning model, and conducted prediction evaluation experiments using measured data using the federated learning method and the conventional method. From the experimental results, we show that the proposed method solves the conventional problems and achieves the same level of prediction accuracy as the conventional method. In addition, we proposed a method of transferring the learning model as a way to improve the prediction accuracy of the throughput in the proposed method, and evaluate the effect of this method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Federated Learning / Deep-Neural-Network / TCP Throughput / Crowd Sensing / Android / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 227, SR2021-53, pp. 72-78, Nov. 2021. | |
| Paper # | SR2021-53 | |
| Date of Issue | 2021-10-28 (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 | SR2021-53 |
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| Conference Information | |
| Committee | SR |
| Conference Date | 2021-11-04 - 2021-11-05 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Software Defined Radio, Cognitive Radio, Spectrum Shareing, etc. |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2021-11-SR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Throughput Prediction by Radio Environment Correlation Recognition Using Crowd Sensing and Federated Learning |
| Sub Title (in English) | |
| Keyword(1) | Federated Learning |
| Keyword(2) | Deep-Neural-Network |
| Keyword(3) | TCP Throughput |
| Keyword(4) | Crowd Sensing |
| Keyword(5) | Android |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Satoshi Nakaniida |
| 1st Author's Affiliation | University of Electro Communications (UEC) |
| 2nd Author's Name | Takeo Fujii |
| 2nd Author's Affiliation | University of Electro Communications (UEC) |
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| Speaker | Author-1 |
| Date Time | 2021-11-05 11:15:00 |
| Presentation Time | 25 minutes |
| Registration for | SR |
| Paper # | SR2021-53 |
| Volume (vol) | vol.121 |
| Number (no) | no.227 |
| Page | pp.72-78 |
| #Pages | 7 |
| Date of Issue | 2021-10-28 (SR) |
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