[21] Ilja Kuzborskij and Christoph Lampert. Data-dependent stability of stochastic gradient descent. In International Conference on Machine Learning, pages 2815-2824. PMLR, 2018.
[22] 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.
[23] Tao Lin, Sebastian U Stich, Kumar Kshitij Patel, and Martin Jaggi. Don't use large mini-batches, use local sgd. arXiv preprint arXiv:1808.07217, 2018.
[24] Yixing Liu, Yan Sun, Zhengtao Ding, Li Shen, Bo Liu, and Dacheng Tao. Enhance local consistency in federated learning: A multi-step inertial momentum approach. arXiv preprint arXiv:2302.05726, 2023.
[25] Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, and Peter Richtarik. From local sgd to local fixed-point methods for federated learning. In International Conference on Machine Learning, pages 6692-6701. PMLR, 2020.
[26] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273-1282. PMLR, 2017.
[27] Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro. A pac-bayesian approach to spectrally-normalized margin bounds for neural networks. arXiv preprint arXiv:1707.09564, 2017.
[28] Emre Ozfatura, Kerem Ozfatura, and Deniz Gunduz. Fedadc: Accelerated federated learning with drift control. In 2021 IEEE International Symposium on Information Theory (ISIT), pages 467-472. IEEE, 2021.
[29] Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, and Zhuo Lu. Generalized federated learning via sharpness aware minimization. In International Conference on Machine Learning, pages 18250-18280. PMLR, 2022.
[30] Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H Brendan McMahan. Adaptive federated optimization. arXiv preprint arXiv:2003.00295, 2020.
[31] Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, and Ali Jabbabaie. Robust federated learning: The case of affine distribution shifts. Advances in Neural Information Processing Systems, 33:21554-21565, 2020.
[32] Naichen Shi, Fan Lai, Raed Al Kontar, and Mosharaf Chowdhury. Fed-ensemble: Improving generalization through model ensembling in federated learning. arXiv preprint arXiv:2107.10663, 2021.
[33] Yifan Shi, Yingqi Liu, Kang Wei, Li Shen, Xueqian Wang, and Dacheng Tao. Make landscape flatter in differentially private federated learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24552-24562, 2023.
[34] Yifan Shi, Li Shen, Kang Wei, Yan Sun, Bo Yuan, Xueqian Wang, and Dacheng Tao. Improving the model consistency of decentralized federated learning. arXiv preprint arXiv:2302.04083, 2023.
[35] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[36] Hao Sun, Li Shen, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, and Dacheng Tao. Adasam: Boosting sharpness-aware minimization with adaptive learning rate and momentum for training deep neural networks. arXiv preprint arXiv:2303.00565, 2023.
[37] Yan Sun, Li Shen, Shixiang Chen, Liang Ding, and Dacheng Tao. Dynamic regularized sharpness aware minimization in federated learning: Approaching global consistency and smooth landscape. arXiv preprint arXiv:2305.11584, 2023.
[38] Yan Sun, Li Shen, Tiansheng Huang, Liang Ding, and Dacheng Tao. Fedspeed: Larger local interval, less communication round, and higher generalization accuracy. arXiv preprint arXiv:2302.10429, 2023.
[39] Yan Sun, Li Shen, Hao Sun, Liang Ding, and Dacheng Tao. Efficient federated learning via local adaptive amended optimizer with linear speedup. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
[40] Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang. Fedproto: Federated prototype learning across heterogeneous clients. In AAAI Conference on Artificial Intelligence, volume 1, 2022.