| 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 | 856 | 2019 |
| Model-Contrastive Federated Learning Q Li, B He, D Song CVPR 2021, 2021 | 731 | 2021 |
| Federated learning on non-iid data silos: An experimental study Q Li*, Y Diao*, Q Chen, B He ICDE 2022, 2022 | 665 | 2022 |
| 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 | 206 | 2018 |
| Practical Federated Gradient Boosting Decision Trees Q Li, Z Wen, B He AAAI 2020, 2020 | 189 | 2020 |
| Practical One-Shot Federated Learning for Cross-Silo Setting Q Li, B He, D Song IJCAI 2021, 2021 | 87* | 2021 |
| Privacy-Preserving Gradient Boosting Decision Trees Q Li, Z Wu, Z Wen, B He AAAI 2020, 2020 | 77 | 2020 |
| 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 | 47 | 2021 |
| 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 | 46 | 2019 |
| 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 | 25 | 2020 |
| Practical vertical federated learning with unsupervised representation learning Z Wu, Q Li, B He IEEE Transactions on Big Data, 2022 | 23 | 2022 |
| 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 | 20 | 2022 |
| A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning Z Wu, Q Li, B He NeurIPS 2022, 2022 | 14 | 2022 |
| Adaptive Kernel Value Caching for SVM Training Q Li, Z Wen, B He IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019 | 13 | 2019 |
| Improving privacy-preserving vertical federated learning by efficient communication with admm C Xie, PY Chen, Q Li, N Arash, C Zhang, B Li SaTML 2024, 2024 | 8 | 2024 |
| Towards Addressing Label Skews in One-shot Federated Learning Y Diao, Q Li, B He ICLR 2023, 2023 | 8 | 2023 |
| DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning Z Wu, J Zhu, Q Li, B He SIGMOD 2023, 2023 | 4 | 2023 |
| FedTree: A Federated Learning System For Trees Q Li, Z Wu, Y Cai, Y Han, CM Yung, T Fu, B He MLSys 2023, 2023 | 4 | 2023 |
| Challenges and Opportunities of Building Fast GBDT Systems Z Wen, Q Li, B He, B Cui IJCAI 2021 Survey, 2021 | 4 | 2021 |
| 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 | 3 | 2023 |
Bingsheng He, 何丙胜Professor, Department of Computer Science, National University of SingaporeVerified email at comp.nus.edu.sg
