| Improving the Model Consistency of Decentralized Federated Learning Y Shi, L Shen, K Wei, Y Sun, B Yuan, X Wang, D Tao The Fortieth International Conference on Machine Learning (ICML), 2023 | 25 | 2023 |
| Make Landscape Flatter in Differentially Private Federated Learning Y Shi, Y Liu, K Wei, L Shen, X Wang, D Tao 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 | 20 | 2023 |
| Efficient federated prompt tuning for black-box large pre-trained models Z Lin, Y Sun, Y Shi, X Wang, L Huang, L Shen, D Tao arXiv preprint arXiv:2310.03123, 2023 | 6 | 2023 |
| Towards more suitable personalization in federated learning via decentralized partial model training Y Shi, Y Liu, Y Sun, Z Lin, L Shen, X Wang, D Tao arXiv preprint arXiv:2305.15157, 2023 | 4 | 2023 |
| Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing Y Shi, K Wei, L Shen, J Li, X Wang, B Yuan, S Guo IEEE Transactions on Mobile Computing, 2024 | 2 | 2024 |
| Towards the flatter landscape and better generalization in federated learning under client-level differential privacy Y Shi, K Wei, L Shen, Y Liu, X Wang, B Yuan, D Tao arXiv preprint arXiv:2305.00873, 2023 | 2 | 2023 |
| Enhancing Personal Decentralized Federated Learning through Model Decoupling Y Shi, Y Liu, Y Sun, Z Lin, L Shen, X Wang, D Tao | | 2023 |
Li ShenSun Yat-sen UniversityVerified email at mail.sysu.edu.cn
