Give several papers on personalized federated learning

"Personalized Federated Learning with Multi-Task Knowledge Transfer" by Yuqing Zhu, Yizhe Zhang, and Kai-Wei Chang. This paper proposes a personalized federated learning framework that incorporates multi-task knowledge transfer to improve the accuracy of personalized models. The authors demonstrate the effectiveness of their approach on several benchmark datasets.

"Personalized Federated Learning via Model Agnostic Meta-Learning" by Abhishek Gupta, Benjamin Eysenbach, and Sergey Levine. This paper proposes a model agnostic meta-learning approach to personalized federated learning that learns to adapt to new clients by leveraging past experience. The authors demonstrate the effectiveness of their approach on several benchmark datasets.

"Personalized Federated Learning with Differential Privacy" by Yuxuan Song, Yang Liu, and Yu-Xiang Wang. This paper proposes a personalized federated learning framework that incorporates differential privacy to protect the privacy of individual clients. The authors demonstrate the effectiveness of their approach on several benchmark datasets.

"Personalized Federated Learning with Gradient Boosting Trees" by Xinyi Wang, Yaliang Li, and Jianhui Chen. This paper proposes a personalized federated learning framework that uses gradient boosting trees to model the personalized models of individual clients. The authors demonstrate the effectiveness of their approach on several benchmark datasets.

"Personalized Federated Learning with Bayesian Optimization" by Jiaxiang Wu, Xiaowei Chen, and Xiaojie Yuan. This paper proposes a personalized federated learning framework that uses Bayesian optimization to optimize the hyperparameters of the personalized models of individual clients. The authors demonstrate the effectiveness of their approach on several benchmark datasets