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| Paper Abstract and Keywords | ||
| Presentation | 2022-05-13 09:00 Optimal Network Selection Method Using Federated Learning for Achieving Both Privacy Preservation and Large-Scale Learning Koki Horita (Sony), Akihiro Nakao (UTokyo)  NS2022-13 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | A smartphone switches from a wireless LAN to a mobile network, including 5G, due to changes in the communication environment. Although it is useful to employ machine learning to learn a model to predict the wireless environment from a large amount of data, it is difficult to utilize data obtained from the market devices because they contain the personal information of users. In this paper, we introduce Federated Learning for learning network selection models. The model enables large-scale learning using market data with user privacy protected and developing a model to predict the quality of the network. We evaluate the performance of these models for optimal network switching by installing them on Android devices. Our proposed method shows that it is effective in improving user experiences by up to 400% by reducing network switching based on advance prediction. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Smartphone / Wi-Fi / Machine Learning / Federated Learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 16, NS2022-13, pp. 23-28, May 2022. | |
| Paper # | NS2022-13 | |
| Date of Issue | 2022-05-05 (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 | NS2022-13 |
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| Conference Information | |
| Committee | NS |
| Conference Date | 2022-05-12 - 2022-05-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Rakuyu Kaikan, Kyoto Univ. + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | High level protocol, Networking technologies (IP and high-layer routing/filtering, Multicast, Quality/Routing control), IP network application technologies (P2P, P4P, Overlay, SIP, NGN), Network system related technologies (System configuration, Interface, Architecture, Hardware/Software/Middleware), Security, Blockchain etc. |
| Paper Information | |
| Registration To | NS |
| Conference Code | 2022-05-NS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Optimal Network Selection Method Using Federated Learning for Achieving Both Privacy Preservation and Large-Scale Learning |
| Sub Title (in English) | |
| Keyword(1) | Smartphone |
| Keyword(2) | Wi-Fi |
| Keyword(3) | Machine Learning |
| Keyword(4) | Federated Learning |
| Keyword(5) | |
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| 1st Author's Name | Koki Horita |
| 1st Author's Affiliation | Sony Corporation (Sony) |
| 2nd Author's Name | Akihiro Nakao |
| 2nd Author's Affiliation | The University of Tokyo (UTokyo) |
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| Speaker | Author-1 |
| Date Time | 2022-05-13 09:00:00 |
| Presentation Time | 25 minutes |
| Registration for | NS |
| Paper # | NS2022-13 |
| Volume (vol) | vol.122 |
| Number (no) | no.16 |
| Page | pp.23-28 |
| #Pages | 6 |
| Date of Issue | 2022-05-05 (NS) |
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