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| Paper Abstract and Keywords | ||
| Presentation | 2021-07-16 13:25 An Evaluation of Learning Accuracy in Federated Learning with Local Differential Privacy Yuta Kakizaki, Koya Sato, Keiichi Iwamura (Tokyo Univ. of Science)  SR2021-37 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In federated learning, where each device learns cooperatively without disclosing the training data, the privacy level can be improved by adding probabilistic noise based on local differential privacy to the training model. On the other hand, since there is a trade-off between the desired privacy level and the learning accuracy, it is possible to achieve both privacy and learning accuracy by training each device independently, depending on the conditions. In this paper, we compare the above two privacy protection methods. We show that the accuracy of the two methods depends on the size of the dataset, and discuss learning design in privacy-constrained environments. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Federated Learning / Differential Privacy / Local Differential Privacy / Machine Learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 104, SR2021-37, pp. 87-93, July 2021. | |
| Paper # | SR2021-37 | |
| Date of Issue | 2021-07-07 (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 | SR2021-37 |
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| Conference Information | |
| Committee | RCS SR NS SeMI RCC |
| Conference Date | 2021-07-14 - 2021-07-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Communication and Network Technology of the AI Age, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2021-07-RCS-SR-NS-SeMI-RCC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An Evaluation of Learning Accuracy in Federated Learning with Local Differential Privacy |
| Sub Title (in English) | |
| Keyword(1) | Federated Learning |
| Keyword(2) | Differential Privacy |
| Keyword(3) | Local Differential Privacy |
| Keyword(4) | Machine Learning |
| Keyword(5) | |
| Keyword(6) | |
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| Keyword(8) | |
| 1st Author's Name | Yuta Kakizaki |
| 1st Author's Affiliation | Tokyo University of Science (Tokyo Univ. of Science) |
| 2nd Author's Name | Koya Sato |
| 2nd Author's Affiliation | Tokyo University of Science (Tokyo Univ. of Science) |
| 3rd Author's Name | Keiichi Iwamura |
| 3rd Author's Affiliation | Tokyo University of Science (Tokyo Univ. of Science) |
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| Speaker | Author-1 |
| Date Time | 2021-07-16 13:25:00 |
| Presentation Time | 25 minutes |
| Registration for | SR |
| Paper # | SR2021-37 |
| Volume (vol) | vol.121 |
| Number (no) | no.104 |
| Page | pp.87-93 |
| #Pages | 7 |
| Date of Issue | 2021-07-07 (SR) |
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