Federated Capsule Graph Neural Networks With Enhanced Privacy Protection

Federated learning (FL) has gained significant traction as a paradigm for decentralized learning, enabling multiple clients to collaboratively train models without sharing their local data. However, applying FL to graph-structured data introduces unique challenges, such as handling non-IID data and preserving the structural dependencies between nodes. Additionally, existing approaches to FL with graph neural networks (GNNs) often struggle to capture complex relationships within graph data and are vulnerable to privacy breaches, including membership inference and model inversion attacks. In this article, we propose federated capsule graph neural networks (FCGNNs), a novel architecture that integrates the dynamic routing capabilities of capsule networks with the structure-preserving power of GNNs in a federated setting. FCGNN is designed to effectively model hierarchical and part-whole relationships within graph data, enabling it to outperform traditional Federated GNN approaches. We enhance the privacy of FCGNN by incorporating differential privacy and secure aggregation techniques, ensuring that individual client updates remain confidential while maintaining strong model performance. We evaluate FCGNN on several benchmark graph datasets, including Cora, Citeseer, PubMed, and PROTEINS, and demonstrate that it consistently achieves higher accuracy and F1-scores compared to existing FL methods. Our experiments show that FCGNN converges faster and incurs lower communication costs, making it highly efficient for real-world FL applications. Furthermore, FCGNN is robust across different numbers of participating clients, maintaining high performance even in non-IID scenarios. These results highlight the potential of FCGNN as a scalable and privacy-preserving solution for decentralized learning on graph-structured data.