Graph neural networks (GNNs) are effective for graph-based node classification tasks, such as data mining and recommendation systems. Combining federated learning(FL) with GNN enables multiple participants to collaboratively train powerful models without sharing private data. However, subgraph-level FL faces challenges, including missing cross-client edges and non-IID data distributions. Additionally, ensuring security in non-fully trusted environments is a critical concern. To address these issues, we propose RMFGL (Robust Meta Federated Graph Learning), a framework for subgraph-level node classification. RMFGL integrates cross-client information through pre-feature aggregation and leverages model-agnostic meta-learning (MAML) to optimize meta-parameters with minimal federated updates. For robustness, we employ a GCN architecture with dual-channel attention aggregation, while Multi-key Fully Homomorphic Encryption (MKFHE) ensures privacy during training. Experimental results on Cora, CiteSeer, PubMed and Coauthor-CS datasets show that RMFGL achieves up to a 2x accuracy improvement with minimal fine-tuning compared to baseline methods and outperforms state-of-the-art techniques. Notably, RMFGL significantly enhances robustness against malicious clients, with up to 100x improvement in stability, while maintaining strong performance with non-IID data.

中文翻译：

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双通道元联邦图学习，具有强大的聚合和隐私增强


图形神经网络 （GNN） 对于基于图形的节点分类任务非常有效，例如数据挖掘和推荐系统。将联邦学习 （FL） 与 GNN 相结合，使多个参与者能够在不共享私人数据的情况下协作训练强大的模型。然而，子图级 FL 面临挑战，包括缺少跨客户端边缘和非 IID 数据分布。此外，确保非完全可信环境中的安全性也是一个关键问题。为了解决这些问题，我们提出了 RMFGL （Robust Meta Federated Graph Learning），这是一个用于子图级节点分类的框架。RMFGL 通过特征前聚合集成跨客户端信息，并利用与模型无关的元学习 （MAML） 以最少的联合更新来优化元参数。为了提高稳健性，我们采用了具有双通道注意力聚合的 GCN 架构，而多密钥全同态加密 （MKFHE） 可确保训练期间的隐私。在 Cora 、 CiteSeer 、 PubMed 和 Coauthor-CS 数据集上的实验结果表明，与基线方法相比，RMFGL 以最少的微调实现了高达 2 倍的准确率提升，并且优于最先进的技术。值得注意的是，RMFGL 显著增强了针对恶意客户端的稳健性，稳定性提高了 100 倍，同时在处理非 IID 数据时保持了强大的性能。