Dual prompt personalized federated learning in foundation models
Abstract Personalized federated learning (PFL) has garnered significant attention for its ability to address heterogeneous client data distributions while preserving data privacy. However, when local client data is limited, deep learning models often suffer from insufficient training, leading to sub...
Saved in:
| Main Authors: | Ying Chang, Xiaohu Shi, Xiaohui Zhao, Zhaohuang Chen, Deyin Ma |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-11864-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
by: Junhui Song, et al.
Published: (2025-07-01) -
Survey of personalized federated learning for edge computing scenarios
by: HE Fan, et al.
Published: (2025-07-01) -
Studying the foundations and documentations of personal status systems in Islamic countries
by: Qiasi Siamak
Published: (2024-01-01) -
Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
by: Rana Albelaihi
Published: (2025-03-01) -
Incentive mechanism of foundation model enabled cross-silo federated learning
by: Ning Zhang, et al.
Published: (2025-07-01)