| Federboost: Private federated learning for gbdt Z Tian, R Zhang, X Hou, J Liu, K Ren arXiv preprint arXiv:2011.02796, 2020 | 58 | 2020 |
| “adversarial examples” for proof-of-learning R Zhang, J Liu, Y Ding, Z Wang, Q Wu, K Ren 2022 IEEE Symposium on Security and Privacy (SP), 1408-1422, 2022 | 23 | 2022 |
| False claims against model ownership resolution J Liu, R Zhang, S Szyller, K Ren, N Asokan arXiv preprint arXiv:2304.06607, 2023 | 3 | 2023 |
| On the robustness of dataset inference S Szyller, R Zhang, J Liu, N Asokan arXiv preprint arXiv:2210.13631, 2022 | 3 | 2022 |
| Towards federated learning of deep graph neural networks Z Tian, Y Ding, R Zhang, J Liu, K Ren | 2 | 2022 |
| : Private Federated Learning for GBDT Z Tian, R Zhang, X Hou, L Lyu, T Zhang, J Liu, K Ren IEEE Transactions on Dependable and Secure Computing, 2023 | | 2023 |
