Privacy-Preserving Node Classification in Customer Services With Federated Graph Neural Networks

As the Internet of Things progresses, the volume of information stored in electronic devices has surged dramatically. Employing Graph Convolutional Networks (GCNs) to model graph data derived from link relationships has led to widespread applications in consumer electronics, such as user preference analysis and personalized recommendations. However, with practical graph data scattered among various owners, traditional centralized GCN methods falter owing to privacy constraints. Thus, creating a distributed GCN framework that upholds model quality and user privacy remains a daunting task. In this paper, we pioneer the use of functional encryption in a federated learning GCN framework, termed FPGCN. This innovative technique achieves performance parity with centralized GCNs, capable of producing equivalent node representations without the need for direct access to raw data. By employing a hierarchical split learning framework coupled with privacy-preserving aggregation mechanisms, it enables secure and collaborative multi-party GCN training, ensuring confidentiality while maintaining model efficacy. Comprehensive experimental results in three real-world datasets demonstrate the capacity of FPGCN to match the predictive accuracy observed in centralized scenarios, while also showcasing its performance efficiency.