| Threats to federated learning L Lyu, H Yu, J Zhao, Q Yang Federated Learning, 3-16, 2020 | 702* | 2020 |
| 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 | 499 | 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 | 456 | 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 | 399 | 2022 |
| Communication-efficient federated learning via knowledge distillation C Wu, F Wu, L Lyu, Y Huang, X Xie Nature communications 13 (1), 2032, 2022 | 373 | 2022 |
| 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 | 362 | 2020 |
| Federated model distillation with noise-free differential privacy L Sun, L Lyu IJCAI’21, 2020 | 323* | 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 | 310 | 2021 |
| Anti-Backdoor Learning: Training Clean Models on Poisoned Data Y Li, X Lyu, N Koren, L Lyu, B Li, X Ma NeurIPS’21, 2021 | 308 | 2021 |
| 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 | 271 | 2020 |
| 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 | 243 | 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 | 243 | 2018 |
| 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 | 227 | 2020 |
| Local differential privacy and its applications: A comprehensive survey KYL Mengmeng Yang, Lingjuan Lyu, Jun Zhao, Tianqing Zhu arxiv, 103827, 2020 | 187* | 2020 |
| Narcissus: A practical clean-label backdoor attack with limited information Y Zeng, M Pan, HA Just, L Lyu, M Qiu, R Jia Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications …, 2023 | 179 | 2023 |
| 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 | 158* | 2022 |
| FLEAM: A federated learning empowered architecture to mitigate DDoS in industrial IoT J Li, L Lyu, X Liu, X Chang, X Lyu IEEE Transactions on Industrial Informatics 18 (6), 4059-4068, 2021 | 136 | 2021 |
| Mobile edge computing, blockchain and reputation-based crowdsourcing IoT federated learning: A secure, decentralized and privacy-preserving system Y Zhao, J Zhao, L Jiang, R Tan, D Niyato, Z Li, L Lyu, Y Liu arXiv preprint arXiv:1906.10893, 2327-4662, 2019 | 133 | 2019 |
| 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 | 133 | 2017 |
| A Federated Graph Neural Network Framework for Privacy-Preserving Personalization Y Huang, C Wu, F Wu, L Lyu, T Qi, X Xie Nature communications, 2022 | 130 | 2022 |