Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features X Shang, Y Lu, G Huang, H Wang International Joint Conference on Artificial Intelligence (IJCAI), 2218-2224, 2022 | 63 | 2022 |
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier Z Li, X Shang, R He, T Lin, C Wu International Conference on Computer Vision (ICCV), 2023 | 37 | 2023 |
Revisiting weighted aggregation in federated learning with neural networks Z Li, T Lin, X Shang, C Wu International Conference on Machine Learning (ICML), 2023 | 35 | 2023 |
Fedic: Federated learning on non-iid and long-tailed data via calibrated distillation X Shang, Y Lu, Y Cheung, H Wang 2022 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2022 | 25 | 2022 |
Federated semi-supervised learning with annotation heterogeneity X Shang, G Huang, Y Lu, J Lou, B Han, Y Cheung, H Wang arXiv preprint arXiv:2303.02445, 2023 | 2 | 2023 |
GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost X Shang, P Sun, T Lin arXiv preprint arXiv:2405.14736, 2024 | | 2024 |
Information Compensation: A Fix for Any-scale Dataset Distillation P Sun, B Shi, X Shang, T Lin | | |
Appendix of No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier Z Li, X Shang, R He, T Lin, C Wu | | |
Understanding the Training Dynamics in Federated Deep Learning via Aggregation Weight Optimization Z Li, T Lin, X Shang, C Wu | | |