[31] Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wenchao Xu, and Feijie Wu. Parameterized knowledge transfer for personalized federated learning. Advances in Neural Information Processing Systems, 34:10092-10104, 2021.
[32] Huancheng Chen, Haris Vikalo, et al. The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-free Hyper-Knowledge Distillation. arXiv preprint arXiv:2301.08968, 2023.
[33] Meng Wang, Kai Yu, Chun-Mei Feng, Yiming Qian, Ke Zou, Lianyu Wang, Rick Siow Mong Goh, Xinxing Xu, Yong Liu, and Huazhu Fu. TrFedDis: Trusted Federated Disentangling Network for Non-IID Domain Feature. arXiv preprint arXiv:2301.12798, 2023.
[34] Guile Wu and Shaogang Gong. Collaborative optimization and aggregation for decentralized domain generalization and adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6484-6493, 2021.
[35] Arthur Jacot, Franck Gabriel, and Clément Hongler. Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural information processing systems, 31, 2018.
[36] Tim Salimans and Durk P Kingma. Weight normalization: A simple reparameterization to accelerate training of deep neural networks. Advances in neural information processing systems, 29, 2016.
[37] Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh. Gradient descent provably optimizes over-parameterized neural networks. arXiv preprint arXiv:1810.02054, 2018.
[38] Yonatan Dukler, Quanquan Gu, and Guido Montúfar. Optimization theory for relu neural networks trained with normalization layers. In International conference on machine learning, pages 2751-2760. PMLR, 2020.
[39] Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečny, Sanjiv Kumar, and H Brendan McMahan. Adaptive federated optimization. arXiv preprint arXiv:2003.00295, 2020.
[40] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
[41] Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman. Geodesic flow kernel for unsupervised domain adaptation. In 2012 IEEE conference on computer vision and pattern recognition, pages 2066-2073. IEEE, 2012.
[42] Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1406-1415, 2019.
[43] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. Reading digits in natural images with unsupervised feature learning. 2011.
[44] Jonathan J. Hull. A database for handwritten text recognition research. IEEE Transactions on pattern analysis and machine intelligence, 16(5):550-554, 1994.
[45] Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International conference on machine learning, pages 1180-1189. PMLR, 2015.
[46] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
[47] Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama. Federated learning based on dynamic regularization. arXiv preprint arXiv:2111.04263, 2021.
[48] Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, and Ethan Fetaya. Personalized federated learning with gaussian processes. Advances in Neural Information Processing Systems, 34:8392-8406, 2021.