<p>Core parameters settings.</p>

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.联邦学习（Federated Learning）能够在分布式智能设备间开展协同模型训练，同时保障数据隐私。在智能医疗网络中，医疗机构可借助图神经网络（Graph Neural Networks，GNNs）基于分布式患者数据开展联合学习，该方法可在不泄露患者隐私的前提下提升诊断准确率。然而，联邦图神经网络仍面临诸多严峻挑战，具体包括梯度隐私漏洞、同态加密带来的计算开销，以及易遭受拜占庭攻击（Byzantine Attacks）的问题。本文提出FedGraphHE，一种面向安全协同智能的隐私保护型联邦图神经网络框架。本文方法整合了三个协同工作的模块：其一，动态自适应分区同态加密（Dynamic Adaptive Partitioned Homomorphic Encryption，DAPHE）可优化梯度传输流程；其二，分层多尺度自适应图Transformer（Hierarchical Multi-scale Adaptive Graph Transformer，HMAGT）能够支持感知加密的图处理任务；其三，基于同态内积的联邦鲁棒聚合（Federated Robust Aggregation via Homomorphic Inner Product，FRAHIP）可实现抗拜占庭攻击的聚合操作。实验结果验证了FedGraphHE在多种场景下的有效性：在引用网络基准数据集（Cora、CiteSeer、PubMed）上，该框架的性能始终优于现有隐私保护方法；在医学影像数据集ISIC 2020上，该框架的分类准确率可达98.18%；与现有同态加密基准方法相比，其通信开销降低约25%。在遭受拜占庭攻击时，该框架仍可保持95%以上的分类准确率，足以证明其是面向隐私敏感型协同学习应用的高效解决方案。