[8] Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. Personalized federated learning: A meta-learning approach. In NeruIPS, 2020.
[9] Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi. Ensemble distillation for robust model fusion in federated learning. In NeurIPS, 2020.
[10] Yiqing Shen, Yuyin Zhou, and Lequan Yu. Cd2-pfed: Cyclic distillation-guided channel decoupling for model personalization in federated learning. In CVPR, 2022.
[11] Alysa Ziying Tan, Han Yu, Lizhen Cui, and Qiang Yang. Towards personalized federated learning. IEEE Transactions on Neural Networks and Learning Systems, 2022.
[12] Yoonho Lee, Annie S Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, and Chelsea Finn. Surgical fine-tuning improves adaptation to distribution shifts. In International Conference on Learning Representations, 2023.
[13] Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt. Test-time training with self-supervision for generalization under distribution shifts. In International conference on machine learning, pages 9229-9248. PMLR, 2020.
[14] Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, and Shirui Pan. Learning strong graph neural networks with weak information. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.
[15] Marvin Zhang, Sergey Levine, and Chelsea Finn. MEMO: Test time robustness via adaptation and augmentation. Advances in Neural Information Processing Systems, 35:38629-38642, 2022.
[16] Dinh C Nguyen, Quoc-Viet Pham, Pubudu N Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia Dobre, and Won-Joo Hwang. Federated learning for smart healthcare: A survey. ACM Computing Surveys (CSUR), 55(3):1-37, 2022.
[17] Guodong Long, Tao Shen, Yue Tan, Leah Gerrard, Allison Clarke, and Jing Jiang. Federated learning for privacy-preserving open innovation future on digital health. In *Humanity Driven AI: Productivity, Well-being, Sustainability and Partnership*, pages 113–133. Springer, 2021.
[18] Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou. Fedbn: Federated learning on non-iid features via local batch normalization. In ICLR, 2020.
[19] Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao. A survey on federated learning. Knowledge-Based Systems, 216:106775, 2021.
[20] Hong-You Chen and Wei-Lun Chao. On bridging generic and personalized federated learning for image classification. In ICLR, 2022.
[21] Xin Dong, Sai Qian Zhang, Ang Li, and HT Kung. Spherefed: Hyperspherical federated learning. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXVI, pages 165-184. Springer, 2022.
[22] Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. Exploiting shared representations for personalized federated learning. In ICML, 2021.
[23] Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. Federated learning with personalization layers. arXiv:1912.00818, 2019.
[24] Paul Pu Liang, Terrance Liu, Liu Ziyin, Ruslan Salakhutdinov, and Louis-Philippe Morency. Think locally, act globally: Federated learning with local and global representations. NeurIPS, 2020.
[25] Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wenchao Xu, and Feijie Wu. Parameterized knowledge transfer for personalized federated learning. *NeruIPS*, 34, 2021.
[26] Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang. FedProto: Federated prototype learning across heterogeneous clients. In AAAI, 2022.