| Privacy-preserving blockchain-based federated learning for IoT devices Y Zhao, J Zhao, L Jiang, R Tan, D Niyato, Z Li, L Lyu, Y Liu IEEE Internet of Things Journal 8 (3), 1817-1829, 2020 | 409* | 2020 |
| Threats to federated learning: A survey L Lyu, H Yu, Q Yang FL-IJCAI’20, 2020 | 332 | 2020 |
| Neural attention distillation: Erasing backdoor triggers from deep neural networks Y Li, X Lyu, N Koren, L Lyu, B Li, X Ma ICLR’21, 2021 | 219 | 2021 |
| PPFA: Privacy preserving fog-enabled aggregation in smart grid L Lyu, K Nandakumar, B Rubinstein, J Jin, J Bedo, M Palaniswami IEEE Transactions on Industrial Informatics 14 (8), 3733-3744, 2018 | 218 | 2018 |
| Local differential privacy-based federated learning for internet of things Y Zhao, J Zhao, M Yang, T Wang, N Wang, L Lyu, D Niyato, KY Lam IEEE Internet of Things Journal 8 (11), 8836-8853, 2020 | 200 | 2020 |
| Towards fair and privacy-preserving federated deep models L Lyu, J Yu, K Nandakumar, Y Li, X Ma, J Jin, H Yu, KS Ng IEEE Transactions on Parallel and Distributed Systems 31 (11), 2524-2541, 2020 | 179 | 2020 |
| Fedgnn: Federated graph neural network for privacy-preserving recommendation C Wu, F Wu, Y Cao, L Lyu, Y Huang, X Xie FL-ICML’21 Oral, 2021 | 176 | 2021 |
| Privacy and robustness in federated learning: Attacks and defenses L Lyu, H Yu, X Ma, C Chen, L Sun, J Zhao, Q Yang, PS Yu TNNLS, 2022 | 174 | 2022 |
| Communication-efficient federated learning via knowledge distillation C Wu, F Wu, L Lyu, Y Huang, X Xie Nature communications 13 (1), 2032, 2022 | 128 | 2022 |
| Anti-Backdoor Learning: Training Clean Models on Poisoned Data Y Li, X Lyu, N Koren, L Lyu, B Li, X Ma NeurIPS’21, 2021 | 125 | 2021 |
| Data poisoning attacks on federated machine learning G Sun, Y Cong, J Dong, Q Wang, L Lyu, J Liu IEEE Internet of Things Journal, 2021 | 122 | 2021 |
| Fog-empowered anomaly detection in IoT using hyperellipsoidal clustering L Lyu, J Jin, S Rajasegarar, X He, M Palaniswami IEEE Internet of Things Journal 4 (5), 1174-1184, 2017 | 117 | 2017 |
| Collaborative fairness in federated learning L Lyu, X Xu, Q Wang, H Yu Federated Learning: Privacy and Incentive; FL-IJCAI’20 (🏆 Best Paper Award …, 2020 | 107 | 2020 |
| Local differential privacy and its applications: A comprehensive survey M Yang, L Lyu, J Zhao, T Zhu, KY Lam arXiv preprint arXiv:2008.03686, 2020 | 95 | 2020 |
| Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification J Zhou, C Chen, L Zheng, H Wu, J Wu, X Zheng, B Wu, L Lyu, Z Liu, ... IJCAI'22, 2022 | 94* | 2022 |
| Federated model distillation with noise-free differential privacy L Sun, L Lyu IJCAI’21, 2020 | 80 | 2020 |
| FLEAM: A federated learning empowered architecture to mitigate DDoS in industrial IoT J Li, L Lyu, X Liu, X Zhang, X Lyu IEEE Transactions on Industrial Informatics 18 (6), 4059-4068, 2021 | 71 | 2021 |
| Threats to federated learning L Lyu, H Yu, J Zhao, Q Yang Federated Learning, 3-16, 2020 | 66 | 2020 |
| Privacy-preserving collaborative deep learning with application to human activity recognition L Lyu, X He, YW Law, M Palaniswami Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 63 | 2017 |
| Fog-embedded deep learning for the Internet of Things L Lyu, JC Bezdek, X He, J Jin IEEE Transactions on Industrial Informatics 15 (7), 4206-4215, 2019 | 51 | 2019 |
