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 | 830 | 2021 |
Privacy-preserving gradient boosting decision trees Q Li, Z Wu, Z Wen, B He Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 34 (01 …, 2020 | 76 | 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 | 47 | 2022 |
Practical vertical federated learning with unsupervised representation learning Z Wu, Q Li, B He IEEE Transactions on Big Data, 2022 | 22 | 2022 |
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) 35, 21087-21100, 2022 | 13 | 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) 1 (2), 1-26, 2023 | 4 | 2023 |
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) 5, 2023 | 4 | 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 | 1 | 2024 |