PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning.
Yu Feng|Yangli-ao Geng|Yifan Zhu|Zongfu Han|Xie Yu|Kaiwen Xue|Haoran Luo|Mengyang Sun|Guangwei Zhang|Meina Song
| Anthology ID: | DBLP:conf/www/FengG0HYX0SZS25 |
|---|---|
| Volume: | Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025 |
| Year: | 2025 |
| Venue: | The Web Conference (WWW) |
| Publisher: | ACM |
| Pages: | 134-146 |
| URL: | https://doi.org/10.1145/3696410.3714561 |
| DOI: | https://doi.org/10.1145/3696410.3714561 |
| DBLP: | conf/www/FengG0HYX0SZS25 |
| BibTeX: |
@inproceedings{feng-2025-pmmoe,
author = {Yu Feng and
Yangli-ao Geng and
Yifan Zhu and
Zongfu Han and
Xie Yu and
Kaiwen Xue and
Haoran Luo and
Mengyang Sun and
Guangwei Zhang and
Meina Song},
editor = {Guodong Long and
Michale Blumestein and
Yi Chang and
Liane Lewin-Eytan and
Zi Huang and
Elad Yom-Tov},
title = {{PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning}},
booktitle = {{Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025}},
pages = {134--146},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3696410.3714561},
doi = {https://doi.org/10.1145/3696410.3714561}
}