| Presentation | 2022-10-05 [Invited Lecture] Reducing Device Processing Load and Communication Overhead by Distillation in Federated Learning Hiromichi Yajima, Takumi Miyoshi, Taku Yamazaki, Shota Ono, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In recent years, machine learning has been used in many cases to discover the rules or to predict future results from a large amount of data. Although current machine learning commonly aggregates data centrally on a server, the drastic increase in the data for machine learning makes it difficult to calculate on a single server. Therefore, distributed machine learning such as federated learning has been attracting attention to avoid the concentrated load on the server. Nevertheless, since the process of machine learning requires a huge amount of computation, it is difficult to perform federated learning process on small devices. This paper proposes a method to reduce device processing load and communication overhead by distillation in federated learning. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Machine learning / Federated learning / Distillation / Processing load / Communication overhead |
| Paper # | NS2022-87 |
| Date of Issue | 2022-09-28 (NS) |
| Conference Information | |
| Committee | NS |
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| Conference Date | 2022/10/5(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaidou University + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Network architecture (5G, Local 5G, Beyond5G, Mobile networks, Ad-hoc and sensor networks, Overlay and P2P networks, Programmable networks, SDN/NFV, IoT, Network slicing), Next generation packet transport (High speed Ethernet, IP over WDM, Multi-service package technology, MPLS), Grid, etc. |
| Chair | Tetsuya Oishi(NTT) |
| Vice Chair | Takumi Miyoshi(Shibaura Insti of Tech.) |
| Secretary | Takumi Miyoshi(NTT) |
| Assistant | Kotaro Mihara(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Network Systems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Invited Lecture] Reducing Device Processing Load and Communication Overhead by Distillation in Federated Learning |
| Sub Title (in English) | |
| Keyword(1) | Machine learning |
| Keyword(2) | Federated learning |
| Keyword(3) | Distillation |
| Keyword(4) | Processing load |
| Keyword(5) | Communication overhead |
| 1st Author's Name | Hiromichi Yajima |
| 1st Author's Affiliation | Shibaura Institute of Technology(Shibaura Inst. of Tech.) |
| 2nd Author's Name | Takumi Miyoshi |
| 2nd Author's Affiliation | Shibaura Institute of Technology(Shibaura Inst. of Tech.) |
| 3rd Author's Name | Taku Yamazaki |
| 3rd Author's Affiliation | Shibaura Institute of Technology(Shibaura Inst. of Tech.) |
| 4th Author's Name | Shota Ono |
| 4th Author's Affiliation | The University of Tokyo(The Univ. of Tokyo) |
| Date | 2022-10-05 |
| Paper # | NS2022-87 |
| Volume (vol) | vol.122 |
| Number (no) | NS-198 |
| Page | pp.pp.29-32(NS), |
| #Pages | 4 |
| Date of Issue | 2022-09-28 (NS) |