FedHeGA: A Federated Learning Framework for Enhanced Node Classification on Heterogeneous Graphs

Heterogeneous graph neural networks (HGNNs) have proven effective at capturing complex relationships in graphs with diverse node and edge types. However, centralized training in HGNNs raises privacy concerns, as sensitive data can be exposed during model training. This risk is further exacerbated when the data is distributed across multiple clients. Federated learning (FL) offers a potential solution by enabling collaborative training without sharing local data. However, existing FL methods for heterogeneous graphs fail to effectively address challenges such as data imbalance and the handling of private edge types. Moreover, existing methods designed for homogeneous graphs are ineffective at addressing the data sparsity issue in heterogeneous graphs. In this article, we propose FedHeGA, a FL framework for heterogeneous graphs that enhances node classification performance while preserving privacy. We tackle data imbalance by integrating heterogeneous graph reconstruction with differential autoencoders to generate semantically coherent node features, improving feature propagation in sparse or imbalanced data. To preserve privacy, we propose a parameter decomposition mechanism that uploads only edge-type-independent global parameters, protecting sensitive local data. Additionally, we address dataset skewness by employing a contrastive learning strategy to align local and global model parameters, which enhances convergence. Experimental results demonstrate that FedHeGA significantly outperforms existing methods on node classification benchmarks, offering an effective solution for federated heterogeneous graph learning.