Survey on Privacy-preserving Techniques for Graph Neural Networks in Federated Learning Paradigm
Zhe Sun, Zhenyu Zhao, Rundong Shao, Y. P. Zou, Chao Li, Nan Wei · 2024
Federated learning, as an emerging distributed machine learning paradigm, allows multiple parties to jointly train models without sharing raw data, thus solving the data silo problem and enhancing data privacy protection. As a powerful tool for processing graph-structured data, graph neural networks (GNNs) have shown great potential in many fields. Federated graph neural networks (FGNNs) combine the advantages of both, allowing data from different institutions to remain localized while utilizing GNNs to model complex graph-structured data, promoting multi-party collaborative training without directly exchanging data. However, the application of FGNNs in distributed environments faces many challenges, especially in protecting data privacy. This survey aims to comprehensively explore the privacy protection technology of FGNNs, covering the privacy protection mechanism of nodes, edges, and models, while also discussing the benefits and drawbacks of each approach. Finally, the future research directions of privacy protection technology in federated graph neural networks are discussed.