| Paper Abstract and Keywords |
| Presentation |
2021-11-16 11:30
Throughput Prediction by Radio Environment Correlation Recognition Using Crowd Sensing and Federated Learning
Satoshi Nakaniida, Takeo Fujii (UEC) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
We propose an approach using federated learning for predicting Wi-Fi and LTE transmission control protocol (TCP) throughput to reduce the delay between the output of prediction results and the problem of security risks by sharing the datasets, which is a problem with conventional machine learning methods. In this study, we constructed a machine learning model, implemented the federated learning model, and conducted prediction evaluation experiments using measured data using the federated learning method and the conventional method. From the experimental results, we show that the proposed method solves the conventional problems and achieves the same level of prediction accuracy as the conventional method. And, the prediction object range of the machine learning model was examined from the viewpoint of the problem assumed by the proposed technique. In addition, we proposed a method of transferring the learning model as a way to improve the prediction accuracy of the throughput in the proposed method, and evaluate the effect of this method. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Federated Learning / Deep-Neural-Network / TCP Throughput / Crowd Sensing / Android / / / |
| Reference Info. |
IEICE Tech. Rep. |
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| Conference Information |
| Committee |
RISING |
| Conference Date |
2021-11-15 - 2021-11-17 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
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| Topics (in Japanese) |
(See Japanese page) |
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| Paper Information |
| Registration To |
RISING |
| Conference Code |
2021-11-RISING |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Throughput Prediction by Radio Environment Correlation Recognition Using Crowd Sensing and Federated Learning |
| Sub Title (in English) |
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| Keyword(1) |
Federated Learning |
| Keyword(2) |
Deep-Neural-Network |
| Keyword(3) |
TCP Throughput |
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Crowd Sensing |
| Keyword(5) |
Android |
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| 1st Author's Name |
Satoshi Nakaniida |
| 1st Author's Affiliation |
The University of The University of Electro Communications (UEC) |
| 2nd Author's Name |
Takeo Fujii |
| 2nd Author's Affiliation |
The University of The University of Electro Communications (UEC) |
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| Speaker |
Author-1 |
| Date Time |
2021-11-16 11:30:00 |
| Presentation Time |
50 minutes |
| Registration for |
RISING |
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| Volume (vol) |
vol. |
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