A vertical federated learning framework for graph convolutional network X Ni, X Xu, L Lyu, C Meng, W Wang arXiv preprint arXiv:2106.11593, 2021 | 37 | 2021 |
Sad: Semi-supervised anomaly detection on dynamic graphs S Tian, J Dong, J Li, W Zhao, X Xu, B Song, C Meng, T Zhang, L Chen arXiv preprint arXiv:2305.13573, 2023 | 7 | 2023 |
Differentially private learning with per-sample adaptive clipping T Xia, S Shen, S Yao, X Fu, K Xu, X Xu, X Fu Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10444 …, 2023 | 5 | 2023 |
Heterogeneous graph node classification with multi-hops relation features X Xu, L Lyu, H Jin, W Wang, S Jia ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 2 | 2022 |
Privacy-preserving design of graph neural networks with applications to vertical federated learning R Wu, M Zhang, L Lyu, X Xu, X Hao, X Fu, T Liu, T Zhang, W Wang arXiv preprint arXiv:2310.20552, 2023 | 1 | 2023 |
FedPSE: Personalized Sparsification with Element-wise Aggregation for Federated Learning XH Longfei Zheng,Yingting Liu,Xiaolong Xu,Chaochao Chen,Yuzhou Tang,Lei Wang CIKM 2023, Pages 3514–3523, 2023 | 1* | 2023 |
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention X Xu, L Lyu, Y Dong, Y Lu, W Wang, H Jin arXiv preprint arXiv:2301.12885, 2023 | 1 | 2023 |