| Paper Abstract and Keywords |
| Presentation |
2024-06-06 13:20
Enhanced Security with Random Binary Weights for Privacy-Preserving Federated Learning
Hiroto Sawada, Shoko Imaizumi (Chiba Univ.), Hitoshi Kiya (TMU) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
In this paper, we propose a novel method for enhancing security in privacy-preserving federated learning under the use of the vision transformer. In federated learning, learning is performed by collecting updated information without collecting raw data from each client. However, the problem is that raw data may be inferred from updated information.
To address this issue, conventional data guessing countermeasures (security enhancement methods) have a trade-off relationship between privacy protection strength and learning efficiency, and generally degrade model performance. In this paper, we propose a novel method of federated learning that does not degrade model performance and is robust against data guessing attacks on updated information. In the proposed method, each client independently prepares a sequence of binary (0 or 1) random numbers, multiplies it by the update information, and sends it to the server for model learning. In experiments, the effectiveness of the proposed method is confirmed in terms of model performance and resistance to the APRIL (Attention PRIvacy Leakage) restoration attack. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
federated learning / vision transformer / privacy preserving / gradient leakage attack resilient / / / / |
| Reference Info. |
ITE Tech. Rep. |
| Paper # |
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| Date of Issue |
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| ISSN |
Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
ME IST IEICE-BioX IEICE-SIP IEICE-MI IEICE-IE |
| Conference Date |
2024-06-06 - 2024-06-07 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Nigata University (Ekinan-Campus "TOKIMATE") |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
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| Paper Information |
| Registration To |
IEICE-SIP |
| Conference Code |
2024-06-ME-IST-BioX-SIP-MI-IE |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Enhanced Security with Random Binary Weights for Privacy-Preserving Federated Learning |
| Sub Title (in English) |
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| Keyword(1) |
federated learning |
| Keyword(2) |
vision transformer |
| Keyword(3) |
privacy preserving |
| Keyword(4) |
gradient leakage attack resilient |
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| 1st Author's Name |
Hiroto Sawada |
| 1st Author's Affiliation |
Chiba University (Chiba Univ.) |
| 2nd Author's Name |
Shoko Imaizumi |
| 2nd Author's Affiliation |
Chiba University (Chiba Univ.) |
| 3rd Author's Name |
Hitoshi Kiya |
| 3rd Author's Affiliation |
Tokyo Metropolitan University (TMU) |
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| Speaker |
Author-1 |
| Date Time |
2024-06-06 13:20:00 |
| Presentation Time |
25 minutes |
| Registration for |
IEICE-SIP |
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| Volume (vol) |
vol.48 |
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