| 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), 2021 | 1365 | 2021 |
| Privacy-preserving gradient boosting decision trees Q Li, Z Wu, Z Wen, B He Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020) 34 …, 2020 | 96 | 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) 13 (4), 1-32, 2022 | 62 | 2022 |
| Practical vertical federated learning with unsupervised representation learning Z Wu, Q Li, B He IEEE Transactions on Big Data, 2022 | 43 | 2022 |
| FedTree: A Federated Learning System For Trees Q Li, Z Wu, Y Cai, CM Yung, T Fu, B He Proceedings of Machine Learning and Systems (MLSys 2023) 5, 2023 | 32* | 2023 |
| A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning Z Wu, Q Li, B He Advances in Neural Information Processing Systems (NeurIPS 2022), 2022 | 26* | 2022 |
| DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning Z Wu, J Zhu, Q Li, B He Proceedings of the ACM on Management of Data (SIGMOD 2023) 1 (2), 1-26, 2023 | 10 | 2023 |
| Vertibench: Advancing feature distribution diversity in vertical federated learning benchmarks Z Wu, J Hou, B He The Twelfth International Conference on Learning Representations (ICLR 2024), 2024 | 4 | 2024 |
| Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data Z Wu, J Hou, Y Diao, B He Advances in Neural Information Processing Systems (NeurIPS 2024), 2024 | 1 | 2024 |
| Federated Data-Efficient Instruction Tuning for Large Language Models Z Qin, Z Wu, B He, S Deng arXiv preprint arXiv:2410.10926, 2024 | 1 | 2024 |
| Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures Y Zhang, Z Qin, Z Wu, S Deng arXiv preprint arXiv:2411.19128, 2024 | | 2024 |
| Model-Based Differentially Private Knowledge Transfer for Large Language Models Z Wu, J Guo, J Hou, B He, L Fan, Q Yang arXiv preprint arXiv:2410.10481, 2024 | | 2024 |
Bingsheng He, 何丙胜Professor, Department of Computer Science, National University of SingaporeAdresse e-mail validée de comp.nus.edu.sg