| Presentation | 2021-07-16 An Evaluation of Learning Accuracy in Federated Learning with Local Differential Privacy Yuta Kakizaki, Koya Sato, Keiichi Iwamura, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | Federated Learning / Differential Privacy / Local Differential Privacy / Machine Learning |
| Paper # | SR2021-37 |
| Date of Issue | 2021-07-07 (SR) |
| Conference Information | |
| Committee | RCS / SR / NS / SeMI / RCC |
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| Conference Date | 2021/7/14(3days) |
| 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 |
| Chair | Eiji Okamoto(Nagoya Inst. of Tech.) / Suguru Kameda(Hiroshima Univ.) / Akihiro Nakao(Univ. of Tokyo) / Koji Yamamoto(Kyoto Univ.) / HUAN-BANG LI(NICT) |
| Vice Chair | Toshihiko Nishimura(Hokkaido Univ.) / Tomoya Tandai(Toshiba) / Fumihide Kojima(NICT) / Osamu Takyu(Shinshu Univ.) / Kentaro Ishidu(NICT) / Kazuto Yano(ATR) / Tetsuya Oishi(NTT) / Kazuya Monden(Hitachi) / Yasunori Owada(NICT) / Shunichi Azuma(Nagoya Univ.) / Koji Ishii(Kagawa Univ.) |
| Secretary | Toshihiko Nishimura(NEC) / Tomoya Tandai(Panasonic) / Fumihide Kojima(Mie Univ.) / Osamu Takyu(Tokai Univ.) / Kentaro Ishidu(NTT) / Kazuto Yano(NTT) / Tetsuya Oishi(Chuo Univ.) / Kazuya Monden(Cyber Univ.) / Yasunori Owada(Waseda Univ.) / Shunichi Azuma(Osaka Univ.) / Koji Ishii(CRIEPI) |
| Assistant | Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Manabu Sakai(Mitsubishi Electric) / Masashi Iwabuchi(NTT) / Tatsuki Okuyama(NTT DOCOMO) / Mai Ohta(Fukuoka Univ.) / Taichi Ohtsuji(NEC) / WANG Xiaoyan(Ibaraki Univ.) / Akemi Tanaka(MathWorks) / Kotaro Mihara(NTT) / Yuki Katsumata(NTT DOCOMO) / Akihito Taya(Aoyama Gakuin Univ.) / Yu Nakayama(Tokyo Univ. of Agri. and Tech.) / SHAN LIN(NICT) / Masaki Ogura(Osaka Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Radio Communication Systems / Technical Committee on Smart Radio / Technical Committee on Network Systems / Technical Committee on Sensor Network and Mobile Intelligence / Technical Committee on Reliable Communication and Control |
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| Language | JPN |
| 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 |
| 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) |
| Date | 2021-07-16 |
| Paper # | SR2021-37 |
| Volume (vol) | vol.121 |
| Number (no) | SR-104 |
| Page | pp.pp.87-93(SR), |
| #Pages | 7 |
| Date of Issue | 2021-07-07 (SR) |