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| Presentation | 2023-01-19 13:50 [Short Paper] A Study of decentralized model training method based on traveling model for P2P Federated Learning Kota Maejima, Takayuki Nishio, Asato Yamazaki, Yuko Hara (Tokyo Tech)  SeMI2022-75 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Peer-to-Peer(P2P) Federated Learning is a machine learning method that builds models across clients without sharing training data among clients. When the data distribution is Non-IID(non-Independent and Identically Distributed), the accuracy of the learned model in P2P Federated Learning deteriorates than in centralized machine learning, which gathers datasets in a central server. In this paper, we propose a method to prevent the model accuracy degradation in P2P Federated Learning in the Non-IID setting. In the proposed method, a single model is circulated over the network and trained by each client in turn. By appropriately routing the model based on the label distribution among clients, the model can be well-trained on non-IID data, similarly to when trained on IID data. Our evaluation results show that the proposed method converges faster than baselines, GossipSGD and PDMM-SGD, especially when the data stored by each client is far from the IID data. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Machine Learning / Federated Learning / Non-IID / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 341, SeMI2022-75, pp. 23-24, Jan. 2023. | |
| Paper # | SeMI2022-75 | |
| Date of Issue | 2023-01-12 (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 | SeMI2022-75 |
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| Conference Information | |
| Committee | SeMI SeMI |
| Conference Date | 2023-01-19 - 2023-01-20 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Naruto grand hotel |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2023-01-SeMI-SeMI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study of decentralized model training method based on traveling model for P2P Federated Learning |
| Sub Title (in English) | |
| Keyword(1) | Machine Learning |
| Keyword(2) | Federated Learning |
| Keyword(3) | Non-IID |
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| 1st Author's Name | Kota Maejima |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 2nd Author's Name | Takayuki Nishio |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 3rd Author's Name | Asato Yamazaki |
| 3rd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 4th Author's Name | Yuko Hara |
| 4th Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
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| Speaker | Author-1 |
| Date Time | 2023-01-19 13:50:00 |
| Presentation Time | 10 minutes |
| Registration for | SeMI |
| Paper # | SeMI2022-75 |
| Volume (vol) | vol.122 |
| Number (no) | no.341 |
| Page | pp.23-24 |
| #Pages | 2 |
| Date of Issue | 2023-01-12 (SeMI) |
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