28 citations · 33 across the 4 of their papers we have counts for
4 papers · 1 filter
Privacy-preserving design of graph neural networks with applications to vertical federated learning
Ruofan Wu, Mingyang Zhang, Lingjuan Lyu +6
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, h…
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Qiying Pan, Ruofan Wu, Tengfei Liu +3
Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preservi…
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention
Xiaolong Xu, Lingjuan Lyu, Yihong Dong +3
With the frequent happening of privacy leakage and the enactment of privacy laws across different countries, data owners are reluctant to directly share their raw data and labels w…
A Vertical Federated Learning Framework for Graph Convolutional Network
Xiang Ni, Xiaolong Xu, Lingjuan Lyu +2
Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data. However in most industries, data exists in the form of isolated i…