[12] H. Tang, Z. Li, Z. Peng, and J. Tang, "Blockmix: Meta regularization and self-calibrated inference for metric-based meta-learning," in ACM Multimedia, pp. 610-618, ACM, 2020. 2
[13] O. Makansi, Ö. Çiçek, Y. Marrakchi, and T. Brox, “On exposing the challenging long tail in future prediction of traffic actors,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13147–13157, 2021. 2
[14] H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, "nuscenes: A multimodal dataset for autonomous driving," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11621-11631, 2020. 2
[15] A. Haque, A. Milstein, and L. Fei-Fei, “Illuminating the dark spaces of healthcare with ambient intelligence,” Nature, vol. 585, no. 7824, pp. 193–202, 2020. 2
[16] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” Proceedings of Machine Learning and Systems, vol. 2, pp. 429–450, 2020. 2, 3, 6
[17] D. A. E. Acar, Y. Zhao, R. Matas, M. Mattina, P. Whatmough, and V. Saligrama, “Federated learning based on dynamic regularization,” in International Conference on Learning Representations, 2021. 3
[18] S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, "Scaffold: Stochastic controlled averaging for federated learning," in International Conference on Machine Learning, pp. 5132-5143, PMLR, 2020. 3
[19] T.-M. H. Hsu, H. Qi, and M. Brown, "Measuring the effects of non-identical data distribution for federated visual classification," arXiv preprint arXiv:1909.06335, 2019. 2, 3
[20] J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor, “Tackling the objective inconsistency problem in heterogeneous federated optimization,” Advances in neural information processing systems, vol. 33, pp. 7611–7623, 2020. 2, 3, 6
[21] A. Z. Tan, H. Yu, L. Cui, and Q. Yang, “Towards personalized federated learning,” IEEE Transactions on Neural Networks and Learning Systems, 2022. 2, 3
[22] M. Luo, F. Chen, D. Hu, Y. Zhang, J. Liang, and J. Feng, “No fear of heterogeneity: Classifier calibration for federated learning with non-iid data,” Advances in Neural Information Processing Systems, vol. 34, pp. 5972–5984, 2021. 2
[23] J. Zhang, Z. Li, B. Li, J. Xu, S. Wu, S. Ding, and C. Wu, “Federated learning with label distribution skew via logits calibration,” in International Conference on Machine Learning, pp. 26311–26329, PMLR, 2022. 2
[24] Z. Liu, Z. Miao, X. Zhan, J. Wang, B. Gong, and S. X. Yu, “Large-scale long-tailed recognition in an open world,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2537–2546, 2019. 2, 4, 6
[25] B. Kang, S. Xie, M. Rohrbach, Z. Yan, A. Gordo, J. Feng, and Y. Kalantidis, “Decoupling representation and classifier for long-tailed recognition,” in International Conference on Learning Representations, 2019. 2, 3, 4, 6
[26] J. Wang, W. Zhang, Y. Zang, Y. Cao, J. Pang, T. Gong, K. Chen, Z. Liu, C. C. Loy, and D. Lin, "Seesaw loss for long-tailed instance segmentation," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9695-9704, 2021. 3, 4, 5
[27] J. Tan, X. Lu, G. Zhang, C. Yin, and Q. Li, “Equalization loss v2: A new gradient balance approach for long-tailed object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1685–1694, 2021. 3, 4, 5, 6
[28] Q. Li, B. He, and D. Song, "Model-contrastive federated learning," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10713-10722, 2021. 3