Privacy-Preserving and Fair Training for Federated GNN

Abstract

Graph neural networks (GNNs) have become a dominant paradigm for learning over graph-structured data. To protect data privacy in distributed graph settings, federated GNNs have emerged as a promising solution by enabling collaborative model training without raw data sharing. However, recent studies demonstrate that federated GNNs can inherit and even amplify biases from distributed data, resulting in unfair global models. While state-of-the-art (SOTA) approaches have introduced fairness-aware federated GNN frameworks, they overlook the privacy risks arising from client–server communications during training. To address this gap, we propose SaFeGNN, a Secure and Fair Federated Graph Neural Network framework that jointly enforces privacy protection and fairness guarantees. SaFeGNN secures the communication process via additive secret sharing and client-level differential privacy, achieving stronger security guarantees compared to existing solutions. Experimental results show that SaFeGNN maintains performance close to that of the baseline, with only an additional overhead of 2.7 s and 3.6 MB per global round.