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} }