| PVD-FL: A Privacy-Preserving and Verifiable Decentralized Federated Learning Framework J Zhao, H Zhu, F Wang, R Lu, Z Liu, H Li IEEE Transactions on Information Forensics and Security 17, 2059-2073, 2022 | 11 | 2022 |
| CORK: A privacy-preserving and lossless federated learning scheme for deep neural network J Zhao, H Zhu, F Wang, R Lu, H Li, J Tu, J Shen Information Sciences 603, 190-209, 2022 | 9 | 2022 |
| Security and Privacy Threats to Federated Learning: Issues, Methods, and Challenges J Zhang, H Zhu, F Wang, J Zhao, Q Xu, H Li Security and Communication Networks 2022, 2022 | 3 | 2022 |
| ACCEL: an efficient and privacy-preserving federated logistic regression scheme over vertically partitioned data J Zhao, H Zhu, F Wang, R Lu, H Li, Z Zhou, H Wan Science China Information Sciences 65 (7), 170307, 2022 | 3 | 2022 |
| An efficient and privacy-preserving route matching scheme for carpooling services Q Xu, H Zhu, Y Zheng, J Zhao, R Lu, H Li IEEE Internet of Things Journal 9 (20), 19890-19902, 2022 | 2 | 2022 |
| VFLR: An Efficient and Privacy-Preserving Vertical Federated Framework for Logistic Regression J Zhao, H Zhu, F Wang, R Lu, E Wang, L Li, H Li IEEE Transactions on Cloud Computing, 2023 | 1 | 2023 |
| SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting Framework J Zhao, H Zhu, W Xu, F Wang, R Lu, H Li IEEE Transactions on Information Forensics and Security 18, 1022-1036, 2022 | 1 | 2022 |
