CA-PFL: Client-adaptive Parameter-efficient Fine-tuning for Personalized Federated Learning
Daixin Song, Hui Cai, Haojie Zhang, Biyun Sheng, Jian Zhou, Mang Ye, Fu Xiao
2026年份
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper，每个回答都会注明依据哪几篇。
相关 Paper
- Hourglass: Enabling Efficient Split Federated Learning with Data ParallelismQiang He, Kaibin Wang, Zeqian Dong, Liang Yuan 等EuroSys 2025 · 被引用 3 次
- Beyond Fine-Tuning: Efficient and Effective Fed-Tuning for Mobile/Web UsersBingyan Liu, Yifeng Cai, Hongzhe Bi, Ziqi Zhang 等WWW 2023 · 被引用 14 次
- FedAS: Bridging Inconsistency in Personalized Federated LearningXiyuan Yang, Wenke Huang, Mang YeCVPR 2024 · 被引用 69 次
- FedL2P: Federated Learning to PersonalizeRoyson Lee, Minyoung Kim, Da Li, Xinchi Qiu 等NeurIPS 2023
- PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar ClassifiersSiyuan Wu, Yongzhe Jia, Bowen Liu, Haolong Xiang 等AAAI 2025 · 被引用 6 次
