| 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, B He arXiv preprint arXiv:1907.09693, 2019 | 121* | 2019 |
| ThunderSVM: A fast SVM library on GPUs and CPUs Z Wen, J Shi, Q Li, B He, J Chen The Journal of Machine Learning Research 19 (1), 797-801, 2018 | 107 | 2018 |
| Practical Federated Gradient Boosting Decision Trees Q Li, Z Wen, B He AAAI 2020, 2020 | 34 | 2020 |
| Privacy-Preserving Gradient Boosting Decision Trees Q Li, Z Wu, Z Wen, B He AAAI 2020, 2020 | 17 | 2020 |
| 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 | 17 | 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 | 11 | 2020 |
| Federated learning on non-iid data silos: An experimental study Q Li, Y Diao, Q Chen, B He arXiv preprint arXiv:2102.02079, 2021 | 10 | 2021 |
| The oarf benchmark suite: Characterization and implications for federated learning systems S Hu, Y Li, X Liu, Q Li, Z Wu, B He arXiv preprint arXiv:2006.07856, 2020 | 10 | 2020 |
| Practical One-Shot Federated Learning for Cross-Silo Setting Q Li, B He, D Song IJCAI 2021, 2021 | 7* | 2021 |
| Adaptive Kernel Value Caching for SVM Training Q Li, Z Wen, B He IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019 | 7 | 2019 |
| Model-Contrastive Federated Learning Q Li, B He, D Song CVPR 2021, 2021 | 4 | 2021 |
| Exploiting Record Similarity for Practical Vertical Federated Learning Z Wu, Q Li, B He arXiv preprint arXiv:2106.06312, 2021 | | 2021 |
| Challenges and Opportunities of Building Fast GBDT Systems Z Wen, Q Li, B He, B Cui IJCAI 2021 Survey, 0 | | |
