A Vertical Federated Learning Framework for Graph Convolutional Network
This addresses data privacy and security issues for industries with isolated data islands, offering a practical solution for federated graph learning, though it appears incremental as it adapts existing methods to a specific setting.
The paper tackles the problem of training Graph Neural Networks (GNNs) on vertically partitioned graph data while preserving privacy, proposing FedVGCN, a federated learning framework that uses homomorphic encryption and achieves effective results in node classification tasks, as demonstrated on benchmark data with GraphSage.
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 islands and the data privacy and security is also an important issue. In this paper, we propose FedVGCN, a federated GCN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GCN models. Specifically, we split the computation graph data into two parts. For each iteration of the training process, the two parties transfer intermediate results to each other under homomorphic encryption. We conduct experiments on benchmark data and the results demonstrate the effectiveness of FedVGCN in the case of GraphSage.