| Model-Contrastive Federated Learning Q Li, B He, D Song CVPR 2021, 2021 | 1999 | 2021 |
| A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection Q Li, Z Wen, Z Wu, S Hu, N Wang, Y Li, X Liu, B He IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019 | 1935 | 2019 |
| Federated learning on non-iid data silos: An experimental study Q Li*, Y Diao*, Q Chen, B He ICDE 2022, 2022 | 1616 | 2022 |
| Practical Federated Gradient Boosting Decision Trees Q Li, Z Wen, B He AAAI 2020, 2020 | 292 | 2020 |
| ThunderSVM: A fast SVM library on GPUs and CPUs Z Wen, J Shi, Q Li, B He, J Chen Journal of Machine Learning Research 19 (21), 1-5, 2018 | 245 | 2018 |
| Practical One-Shot Federated Learning for Cross-Silo Setting Q Li, B He, D Song IJCAI 2021, 2021 | 187 | 2021 |
| Privacy-Preserving Gradient Boosting Decision Trees Q Li, Z Wu, Z Wen, B He AAAI 2020, 2020 | 115 | 2020 |
| LLM-PBE: Assessing Data Privacy in Large Language Models Q Li*, J Hong*, C Xie*, J Tan, R Xin, J Hou, X Yin, Z Wang, D Hendrycks, ... VLDB 2024, 2024 | 95 | 2024 |
| Guardagent: Safeguard llm agents by a guard agent via knowledge-enabled reasoning Z Xiang, L Zheng, Y Li, J Hong, Q Li, H Xie, J Zhang, Z Xiong, C Xie, ... arXiv preprint arXiv:2406.09187, 2024 | 85* | 2024 |
| The oarf benchmark suite: Characterization and implications for federated learning systems S Hu, Y Li, X Liu, Q Li, Z Wu, B He ACM Transactions on Intelligent Systems and Technology (TIST), 2021 | 72 | 2021 |
| Practical vertical federated learning with unsupervised representation learning Z Wu, Q Li, B He IEEE transactions on big data 10 (6), 864-878, 2022 | 71 | 2022 |
| Exploiting GPUs for efficient gradient boosting decision tree training Z Wen, J Shi, B He, J Chen, K Ramamohanarao, Q Li IEEE Transactions on Parallel and Distributed Systems 30 (12), 2706-2717, 2019 | 67 | 2019 |
| FedTree: A Federated Learning System For Trees Q Li, Z Wu, Y Cai, Y Han, CM Yung, T Fu, B He MLSys 2023, 2023 | 50 | 2023 |
| SoK: Privacy-Preserving Data Synthesis Y Hu, F Wu, Q Li, Y Long, GM Garrido, C Ge, B Ding, D Forsyth, B Li, ... S&P 2024, 2023 | 45 | 2023 |
| A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning Z Wu, Q Li, B He NeurIPS 2022, 2022 | 45 | 2022 |
| Towards Addressing Label Skews in One-shot Federated Learning Y Diao, Q Li, B He ICLR 2023, 2023 | 43 | 2023 |
| Unifed: A benchmark for federated learning frameworks X Liu, T Shi, C Xie, Q Li, K Hu, H Kim, X Xu, B Li, D Song arXiv preprint arXiv:2207.10308, 2022 | 43 | 2022 |
| Exploiting Label Skews in Federated Learning with Model Concatenation Y Diao, Q Li, B He AAAI 2024, 2024 | 33 | 2024 |
| ThunderGBM: Fast GBDTs and Random Forests on GPUs Z Wen, H Liu, J Shi, Q Li, B He, J Chen The Journal of Machine Learning Research (JMLR), 2020 | 30 | 2020 |
| Adversarial Collaborative Learning on Non-IID Features Q Li, B He, D Song ICML 2023, 2023 | 29 | 2023 |
Bingsheng He, 何丙胜Professor, Department of Computer Science, National University of SingaporeVerified email at comp.nus.edu.sg
