| Learn from others and be yourself in heterogeneous federated learning W Huang, M Ye, B Du CVPR, 10143-10153, 2022 | 265 | 2022 |
| Rethinking Federated Learning with Domain Shift: A Prototype View W Huang, M Ye, Z Shi, L He, B Du CVPR, 16312-16322, 2023 | 125* | 2023 |
| Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark W Huang, M Ye, Z Shi, G Wan, H Li, B Du, Q Yang IEEE TPAMI, 2024 | 64 | 2024 |
| Dynamic Personalized Federated Learning with Adaptive Differential Privacy X Yang, W Huang, M Ye NeurIPS, 2023 | 48 | 2023 |
| Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning W Huang, M Ye, Z Shi, B Du IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023 | 43 | 2023 |
| Federated Graph Semantic and Structural Learning W Huang, G Wan, M Ye, B Du IJCAI, 2023 | 43 | 2023 |
| Few-shot model agnostic federated learning W Huang, M Ye, B Du, X Gao ACM MM, 7309-7316, 2022 | 38 | 2022 |
| Federated Graph Learning under Domain Shift with Generalizable Prototypes G Wan, W Huang, M Ye AAAI 38 (14), 15429-15437, 2024 | 27 | 2024 |
| S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning G Wan, Y Tian, W Huang, NV Chawla, M Ye ICML, 2024 | 14 | 2024 |
| Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity Y Chen, W Huang, M Ye CVPR 2024, 2024 | 13 | 2024 |
| FedAS: Bridging Inconsistency in Personalized Federated Learning X Yang, W Huang, M Ye CVPR, 11986-11995, 2024 | 10 | 2024 |
| Federated Learning with Long-Tailed Data via Representation Unification and Classifier Rectification W Huang, Y Liu, M Ye, J Chen, B Du IEEE TIFS, 2024 | 8 | 2024 |
| FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference Z Tan, G Wan, W Huang, M Ye NeurIPS 2024, 2024 | 5 | 2024 |
| Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning W Huang, Z Shi, M Ye, H Li, B Du ICML, 2024 | 4 | 2024 |
| Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning W Huang, J Liang, Z Shi, D Zhu, G Wan, H Li, B Du, D Tao, M Ye arXiv preprint arXiv:2411.10928, 2024 | 3 | 2024 |
| Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning W Huang, M Ye, Z Shi, B Du, D Tao ECCV, 2024 | 3 | 2024 |
| Resisting Over-Smoothing in Graph Neural Networks via Dual-Dimensional Decoupling W Shen, M Ye, W Huang ACM Multimedia 2024, 2024 | 3 | 2024 |
| Revisiting federated learning with label skew: An overconfidence perspective M Ye, W Huang, Z Shi, H Li, D Bo SCIS, 2024 | 3 | 2024 |
| Label-Aware Calibration and Relation-Preserving in Visual Intention Understanding QHY Shi, M Ye, W Huang, W Ruan, B Du IEEE TIP, 2024 | 3 | 2024 |
| Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning W Huang, M Ye, Z Shi, G Wan, H Li, B Du NeurIPS 2024, 2024 | 1 | 2024 |
Mang YeProfessor, Wuhan UniversityPreverjeni e-poštni naslov na whu.edu.cn