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| Presentation | 2020-01-31 10:00 [Poster Presentation] Communication-Efficient Federated Learning Using Non-Labeled Data Souhei Itahara, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ)  SeMI2019-109 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Federated learning (FL) is a machine learning setting where many mobile devices collaboratively train a machine learning (ML) model, while keeping the training data decentralized. In FL, each device updates a model with his/her data and uploads the model to a server which aggregates the models instead of uploading the training data to the server.Thus, the traffic for uploading the model is not negligible.This paper proposes a cooperative learning method, called Distillation Based Semi-Supervised Federated Learning (DS-FL), which aims to reduce traffic required for training the ML model. In DS-FL, non-labeled open data is used for the cooperative model training via semi-supervised learning.Each device trains a model with his/her data, predicts logits for the open data, and updates the model with the open data and aggregated logits. Since the data size of the logits is much smaller than that of the models, traffic is reduced largely. We evaluate our method using an image classification task (MNIST). Our experiments show that the proposed method achieves 94% less traffic than that of the previous method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Federated Learning / Semi-Supervised Learning / Machine Learning / Communication Cost / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 406, SeMI2019-109, pp. 47-48, Jan. 2020. | |
| Paper # | SeMI2019-109 | |
| Date of Issue | 2020-01-23 (SeMI) | |
| 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 | SeMI2019-109 |
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| Conference Information | |
| Committee | SeMI |
| Conference Date | 2020-01-30 - 2020-01-31 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2020-01-SeMI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Communication-Efficient Federated Learning Using Non-Labeled Data |
| Sub Title (in English) | |
| Keyword(1) | Federated Learning |
| Keyword(2) | Semi-Supervised Learning |
| Keyword(3) | Machine Learning |
| Keyword(4) | Communication Cost |
| Keyword(5) | |
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| 1st Author's Name | Souhei Itahara |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ) |
| 2nd Author's Name | Takayuki Nishio |
| 2nd Author's Affiliation | Kyoto University (Kyoto Univ) |
| 3rd Author's Name | Masahiro Morikura |
| 3rd Author's Affiliation | Kyoto University (Kyoto Univ) |
| 4th Author's Name | Koji Yamamoto |
| 4th Author's Affiliation | Kyoto University (Kyoto Univ) |
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| Speaker | Author-1 |
| Date Time | 2020-01-31 10:00:00 |
| Presentation Time | 90 minutes |
| Registration for | SeMI |
| Paper # | SeMI2019-109 |
| Volume (vol) | vol.119 |
| Number (no) | no.406 |
| Page | pp.47-48 |
| #Pages | 2 |
| Date of Issue | 2020-01-23 (SeMI) |
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