  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 .
