A Privacy-Enhancing Mechanism for Federated Graph Neural Networks
Xuebin Tang, Feng Hu · Symmetry · 2025
In recent years, with the rapid development of the internet, the accumulation of massive data has significantly propelled the advancement of artificial intelligence. Graphs, as an important data structure for representing relationships between entities, are widely used in various real-world scenarios such as social networks and e-commerce platforms. Graph Neural Networks (GNNs) have emerged as a popular research topic, capable of learning information from neighborhoods and extracting features from graph-structured data to solve tasks like graph classification, node classification, and link prediction. However, the centralized training of GNNs often faces challenges due to data isolation and privacy concerns. Federated Learning (FL) has been proposed as a solution to these issues, allowing multiple users to collaboratively train models without sharing raw data. This paper introduces a privacy-preserving mechanism based on Local Differential Privacy (LDP) to enhance the security of Federated Graph Neural Networks (FedGNNs) against inference attacks while maintaining model performance.