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| Paper Abstract and Keywords | ||
| Presentation | 2024-03-13 16:40 A Plug-and-Play Module for Enhancing Fault-Tolerant Distributed Inference Based on Gaussian Dropout Hou Zhangcheng, Ohtsuki Tomoaki (KU)  RCS2023-267 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Distributed inference (DI) in the Internet of Things (IoT) is becoming increasingly important as the demand for AI applications grows. When unreliable links are used for IoT transmission, missing data can adversely affect inference accuracy. Therefore, we designed a plug-and-play module to enhance the robustness of DI systems. The dropout we utilize is no longer a fixed value but is sampled from a Gaussian distribution, making it better adapted to lossy networks. Additionally, the convolutional layer of the module can learn and preserve fault-tolerant inference. This method uses freeze training and does not require retraining of the original deep neural network, thus significantly reducing the number of parameters to be trained and avoiding catastrophic forgetting. We incorporate various designs for the Gaussian dropout layer to further enhance its effectiveness at low packet loss rates. The module only needs to be attached to the cut-points of the original deep neural network, making it very easy and fast to deploy. Experimental results demonstrate that our plug-and-play module can be adapted to different deep neural networks and various lossy network scenarios, achieving an average accuracy improvement in all tests. Results show that our plug-and-play module significantly enhances the fault-tolerant inference of the system. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Internet of Things / error-tolerant / distributed inference / deep learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 434, RCS2023-267, pp. 77-82, March 2024. | |
| Paper # | RCS2023-267 | |
| Date of Issue | 2024-03-06 (RCS) | |
| 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 | RCS2023-267 |
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| Conference Information | |
| Committee | RCS SR SRW |
| Conference Date | 2024-03-13 - 2024-03-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | The University of Tokyo (Hongo Campus), and online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2024-03-RCS-SR-SRW |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Plug-and-Play Module for Enhancing Fault-Tolerant Distributed Inference Based on Gaussian Dropout |
| Sub Title (in English) | |
| Keyword(1) | Internet of Things |
| Keyword(2) | error-tolerant |
| Keyword(3) | distributed inference |
| Keyword(4) | deep learning |
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| 1st Author's Name | Hou Zhangcheng |
| 1st Author's Affiliation | Keio University (KU) |
| 2nd Author's Name | Ohtsuki Tomoaki |
| 2nd Author's Affiliation | Keio University (KU) |
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| Speaker | Author-1 |
| Date Time | 2024-03-13 16:40:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCS2023-267 |
| Volume (vol) | vol.123 |
| Number (no) | no.434 |
| Page | pp.77-82 |
| #Pages | 6 |
| Date of Issue | 2024-03-06 (RCS) |
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