Heterogeneous graph neural networks (HGNNs) can learn from typed and relational graph data more effectively than conventional GNNs. With larger parameter spaces, HGNNs may require more training data, which is often scarce in real-world applications due to privacy regulations (e.g., GDPR). Federated graph learning (FGL) enables multiple clients to train a GNN collaboratively without sharing their local data. However, existing FGL methods mainly focus on homogeneous GNNs or knowledge graph embeddings; few have considered heterogeneous graphs and HGNNs. In federated heterogeneous graph learning, clients may have private graph schemas. Conventional FL/FGL methods attempting to define a global HGNN model would violate schema privacy. To address these challenges, we propose FedHGN, a novel and general FGL framework for HGNNs. FedHGN adopts schema-weight decoupling to enable schema-agnostic knowledge sharing and employs coefficients alignment to stabilize the training process and improve HGNN performance. With better privacy preservation, FedHGN consistently outperforms local training and conventional FL methods on three widely adopted heterogeneous graph datasets with varying client numbers. The code is available at https://github.com/cynricfu/FedHGN .

翻译：异构图神经网络（HGNNs）能比传统GNN更有效地从带类型和关系结构的图数据中学习。由于参数空间更大，HGNNs需要更多训练数据，而在实际应用中，数据常因隐私法规（如GDPR）而稀缺。联邦图学习（FGL）允许多个客户端在不共享本地数据的情况下协作训练GNN。然而，现有FGL方法主要关注同构GNN或知识图谱嵌入，鲜有考虑异构图和HGNNs。在联邦异构图学习中，客户端可能拥有私有图模式。传统的FL/FGL方法试图定义全局HGNN模型，这违反了模式隐私。为解决这些挑战，我们提出FedHGN——一种新颖且通用的面向HGNNs的FGL框架。FedHGN采用模式-权重解耦实现模式无关的知识共享，并通过系数对齐稳定训练过程并提升HGNN性能。在更好的隐私保护下，FedHGN在三个广泛使用的异构图数据集上（包含不同客户端数量）始终优于本地训练和传统FL方法。代码已开源：https://github.com/cynricfu/FedHGN。