| Presentation | 2021-07-16 Improving the Runtime Performance of Decentralized Machine Learning on Wireless Channels via Rate Adaptation Koya Sato, Daisuke Sugimura, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper presents a communication strategy for improving the runtime of decentralized machine learning over wireless networks. An iteration of local training and sharing the trained parameters can realize the decentralized machine learning; however, in the wireless channel, communication time tends to be a bottleneck for the runtime performance owing to path loss and multipath fading. To deal with this problem, we focus on the tradeoff between communication time and training accuracy, which is raised by adjusting the transmission rate. We formulate the rate adaptation as a minimization problem for the communication time under the constraint for the network density. Numerical results demonstrate that the rate adaptation aids in realizing the fast decentralized machine learning. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Decentralized machine learning / rate adaptation / network topology |
| Paper # | RCS2021-94 |
| Date of Issue | 2021-07-07 (RCS) |
| 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) | Improving the Runtime Performance of Decentralized Machine Learning on Wireless Channels via Rate Adaptation |
| Sub Title (in English) | |
| Keyword(1) | Decentralized machine learning |
| Keyword(2) | rate adaptation |
| Keyword(3) | network topology |
| 1st Author's Name | Koya Sato |
| 1st Author's Affiliation | Tokyo University of Science(Tokyo Univ. of Science) |
| 2nd Author's Name | Daisuke Sugimura |
| 2nd Author's Affiliation | Tsuda University(Tsuda Univ.) |
| Date | 2021-07-16 |
| Paper # | RCS2021-94 |
| Volume (vol) | vol.121 |
| Number (no) | RCS-103 |
| Page | pp.pp.80-85(RCS), |
| #Pages | 6 |
| Date of Issue | 2021-07-07 (RCS) |