| Paper Abstract and Keywords |
| Presentation |
2025-02-18 13:35
Effectiveness Verification of Introducing Model Merging in Federated Learning
-- Investigation from Multi-domain Image Classification Tasks --
Kenta Kubota, Reb Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
In this paper, we evaluate the effectiveness of model merging methods for federated learning, which continuously update the pre-trained models.
Model merging, which aims to build a single effective model for various tasks by combining multiple domain-specific models obtained by fine-tuning pre-trained models, has been actively studied in recent years.
Federated learning is a framework in which multiple models collaborate to learn in a data-distributed environment and is expected to be an application of model merging due to its problem setting.
However, previous studies paid little attention to the effectiveness of model merging methods for federated learning, and there is room for further investigation.
Therefore, to clarify the technical issues involved in introducing model merging to federated learning, this paper applies some methods to an image classification task in a federated learning setting and experimentally investigates the impact of model merging on the accuracy of the model. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Model merge / Federated learning / Foundation model / / / / / |
| Reference Info. |
ITE Tech. Rep., vol. 49, no. 4, ME2025-10, pp. 52-56, Feb. 2025. |
| Paper # |
ME2025-10 |
| Date of Issue |
2025-02-11 (MMS, ME, AIT, SIP) |
| ISSN |
Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
ME AIT MMS IEICE-IE IEICE-ITS SIP |
| Conference Date |
2025-02-18 - 2025-02-19 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Hokkaido Univ. |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Image Processing, etc. |
| Paper Information |
| Registration To |
ME |
| Conference Code |
2025-02-ME-AIT-MMS-IE-ITS-SIP |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Effectiveness Verification of Introducing Model Merging in Federated Learning |
| Sub Title (in English) |
Investigation from Multi-domain Image Classification Tasks |
| Keyword(1) |
Model merge |
| Keyword(2) |
Federated learning |
| Keyword(3) |
Foundation model |
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| 1st Author's Name |
Kenta Kubota |
| 1st Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 2nd Author's Name |
Reb Togo |
| 2nd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 3rd Author's Name |
Keisuke Maeda |
| 3rd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 4th Author's Name |
Takahiro Ogawa |
| 4th Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 5th Author's Name |
Miki Haseyama |
| 5th Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
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| Speaker |
Author-1 |
| Date Time |
2025-02-18 13:35:00 |
| Presentation Time |
15 minutes |
| Registration for |
ME |
| Paper # |
MMS2025-10, ME2025-10, AIT2025-10, SIP2025-10 |
| Volume (vol) |
vol.49 |
| Number (no) |
no.4 |
| Page |
pp.52-56 |
| #Pages |
5 |
| Date of Issue |
2025-02-11 (MMS, ME, AIT, SIP) |