Training Time (seconds).

Federated learning (FL) enables collaborative model training across distributed intelligent devices while preserving data privacy. In smart healthcare networks, medical institutions can jointly learn from distributed patient data using graph neural networks (GNNs). This approach improves diagnostic accuracy without compromising patient confidentiality. However, federated GNNs face substantial challenges. These include gradient privacy vulnerabilities, computational overhead from homomorphic encryption, and susceptibility to Byzantine attacks. This paper presents FedGraphHE, a privacy-preserving federated GNN framework for secure collaborative intelligence. Our methodology integrates three synergistic modules. First, Dynamic Adaptive Partitioned Homomorphic Encryption (DAPHE) optimizes gradient transmission. Second, Hierarchical Multi-scale Adaptive Graph Transformer (HMAGT) enables encryption-aware graph processing. Third, Federated Robust Aggregation via Homomorphic Inner Product (FRAHIP) provides Byzantine-resilient aggregation. Experimental results demonstrate FedGraphHE’s effectiveness across multiple scenarios. The framework consistently outperforms existing privacy-preserving methods on citation network benchmarks (Cora, CiteSeer, PubMed). It achieves 98.18% classification accuracy on medical imaging datasets (ISIC 2020), and reduces communication costs by approximately 25% compared to existing homomorphic encryption baselines. The framework maintains over 95% accuracy under Byzantine attacks, establishing it as an effective solution for privacy-sensitive collaborative learning applications.