| Fedspeed: Larger local interval, less communication round, and higher generalization accuracy Y Sun, L Shen, T Huang, L Ding, D Tao The 11th International Conference on Learning Representations (ICLR 2023), 2023 | 55 | 2023 |
| Improving the model consistency of decentralized federated learning Y Shi, L Shen, K Wei, Y Sun, B Yuan, X Wang, D Tao The 40th International Conference on Machine Learning (ICML 2023), 2023 | 52 | 2023 |
| On efficient training of large-scale deep learning models: A literature review L Shen, Y Sun, Z Yu, L Ding, X Tian, D Tao ACM Computing Surveys (ACM CSUR), 2023 | 37 | 2023 |
| Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape Y Sun, L Shen, S Chen, L Ding, D Tao The 40th International Conference on Machine Learning (ICML 2023 Oral), 2023 | 36 | 2023 |
| Subspace based federated unlearning G Li, L Shen, Y Sun, Y Hu, H Hu, D Tao arXiv preprint arXiv:2302.12448, 2023 | 27 | 2023 |
| Visual Prompt Based Personalized Federated Learning G Li, W Wu, Y Sun, L Shen, B Wu, D Tao Transactions on Machine Learning Research (TMLR), 2023 | 24 | 2023 |
| FedGAMMA: Federated Learning With Global Sharpness-Aware Minimization R Dai, X Yang, Y Sun, L Shen, X Tian, M Wang, Y Zhang IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023 | 19 | 2023 |
| Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup Y Sun, L Shen, H Sun, L Ding, D Tao IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023 | 18 | 2023 |
| Fusion of Global and Local Knowledge for Personalized Federated Learning T Huang, L Shen, Y Sun, W Lin, D Tao Transactions on Machine Learning Research (TMLR), 2023 | 17 | 2023 |
| Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization Y Sun, L Shen, D Tao The 37th Conference on Neural Information Processing Systems (NIPS 2023), 2023 | 15 | 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 | 14 | 2023 |
| Which mode is better for federated learning? Centralized or Decentralized Y Sun, L Shen, D Tao arXiv preprint arXiv:2310.03461, 2023 | 9 | 2023 |
| Enhance local consistency in federated learning: A multi-step inertial momentum approach Y Liu, Y Sun, Z Ding, L Shen, B Liu, D Tao arXiv preprint arXiv:2302.05726, 2023 | 8 | 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 | 7 | 2023 |
| A Unified Analysis for Finite Weight Averaging P Wang, L Shen, Z Tao, Y Sun, G Zheng, D Tao arXiv preprint arXiv:2411.13169, 2024 | | 2024 |
| A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs Y Sun, L Shen, D Tao The 38th Conference on Neural Information Processing Systems (NIPS 2024 …, 2024 | | 2024 |
| Convergent Differential Privacy Analysis for General Federated Learning: the -DP Perspective Y Sun, L Shen, D Tao arXiv preprint arXiv:2408.15621, 2024 | | 2024 |
| Enhancing Personal Decentralized Federated Learning through Model Decoupling Y Shi, Y Liu, Y Sun, Z Lin, L Shen, X Wang, D Tao | | |
Li ShenAssociate Professor, Sun Yat-sen University在 mail.sysu.edu.cn 的电子邮件经过验证
