[68] Liangxi Liu, Xi Jiang, Feng Zheng, Hong Chen, Guo-Jun Qi, Heng Huang, and Ling Shao. A bayesian federated learning framework with online laplace approximation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
[69] Michael Zhang, James Lucas, Jimmy Ba, and Geoffrey E Hinton. Lookahead optimizer: k steps forward, 1 step back. Advances in neural information processing systems, 32, 2019.
[70] Alex Nichol, Joshua Achiam, and John Schulman. On First-Order Meta-Learning algorithms. arXiv preprint arXiv:1803.02999, 2018.
[71] Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. On the convergence theory of Gradient-Based Model-Agnostic Meta-Learning algorithms. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 1082-1092. PMLR, 2020.
[72] Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečny, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. LEAF: A benchmark for federated settings. arXiv preprint arXiv:1812.01097, 2018.
[73] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems, 2:429-450, 2020.
[74] Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B Allen, Randy P Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency. Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523, 2020.
[75] Agustinus Kristiadi, Matthias Hein, and Philipp Hennig. Being bayesian, even just a bit, fixes overconfidence in ReLU networks. In Hal Daumé Iii and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 5436-5446. PMLR, 2020.
[76] Noah Weber, Janez Starc, Arpit Mittal, Roi Blanco, and Lluis Marquez. Optimizing over a bayesian last layer, 2018.
[77] Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, and Jan Peters. Latent derivative bayesian last layer networks. In Arindam Banerjee and Kenji Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 1198-1206. PMLR, 2021.
[78] Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. Deep variational information bottleneck. arXiv preprint arXiv:1612.00410, 2016.
[79] Wei Du, Xiao Zeng, Ming Yan, and Mi Zhang. Efficient federated learning via variational dropout. Accessed: 2023-10-21, September 2018.
[80] Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed, Mike Rabbat, Maziar Sanjabi, and Lin Xiao. Federated learning with partial model personalization. In International Conference on Machine Learning, pages 17716-17758. PMLR, 2022.
[81] Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, John Rush, and Sushant Prakash. Federated reconstruction: Partially local federated learning. Advances in Neural Information Processing Systems, 34:11220-11232, 2021.
[82] Dingzhu Wen, Ki-Jun Jeon, and Kaibin Huang. Federated dropout - a simple approach for enabling federated learning on resource constrained devices. IEEE wireless communications letters, 11(5):923-927, 2022.
[83] Gary Cheng, Zachary Charles, Zachary Garrett, and Keith Rush. Does federated dropout actually work? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 3387-3395, 2022.
[84] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. On calibration of modern neural networks. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 1321-1330. PMLR, 2017.
[85] Alexandru Niculescu-Mizil and Rich Caruana. Predicting good probabilities with supervised learning. In Proceedings of the 22nd international conference on Machine learning, pages 625-632, 2005.