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| Paper Abstract and Keywords | ||
| Presentation | 2022-05-27 11:20 [Invited Lecture] Empowering Federated Learning in Vehicular IoT Celimuge Wu (UEC)  IN2022-9 RCS2022-22 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Federated learning is a promising paradigm for achieving distributed intelligence by protecting user privacy in vehicular networks. Considering limited computing and communication resources, it is important to select appropriate clients from a huge number of users to participate in the training process. In vehicular networks, the problem of choosing proper clients is particularly complex due to the heterogeneity of network users, including the differences in the data, computation capability, available throughput, and samples freshness. We design a fuzzy logic based client selection scheme to address this issue. We consider four input variables in the proposed scheme, namely, the quantity of samples, computational capability, available throughput, and sample freshness. Extensive simulations in various scenarios confirm that the proposed scheme is superior to other baselines in terms of both the learning efficiency and communication efficiency. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Federated learning / Client selection / Fuzzy logic / Vehicular IoT / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 48, IN2022-9, pp. 43-48, May 2022. | |
| Paper # | IN2022-9 | |
| Date of Issue | 2022-05-19 (IN, RCS) | |
| 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 | IN2022-9 RCS2022-22 |
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| Conference Information | |
| Committee | IN RCS NV |
| Conference Date | 2022-05-26 - 2022-05-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Keio University (Hiyoshi Campus) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Ad-Hoc/Sensor Networks/MANET, Mobile Networks, M2M/IoT Communications, Wi-Fi, IEEE802.15(ZigBee) and others |
| Paper Information | |
| Registration To | IN |
| Conference Code | 2022-05-IN-RCS-NV |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Empowering Federated Learning in Vehicular IoT |
| Sub Title (in English) | |
| Keyword(1) | Federated learning |
| Keyword(2) | Client selection |
| Keyword(3) | Fuzzy logic |
| Keyword(4) | Vehicular IoT |
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| 1st Author's Name | Celimuge Wu |
| 1st Author's Affiliation | The University of Electro-Communications (UEC) |
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| Speaker | Author-1 |
| Date Time | 2022-05-27 11:20:00 |
| Presentation Time | 25 minutes |
| Registration for | IN |
| Paper # | IN2022-9, RCS2022-22 |
| Volume (vol) | vol.122 |
| Number (no) | no.48(IN), no.49(RCS) |
| Page | pp.43-48(IN), pp.45-50(RCS) |
| #Pages | 6 |
| Date of Issue | 2022-05-19 (IN, RCS) |
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