Horizontal Federated Heterogeneous Graph Learning: A Multi-Scale Adaptive Solution to Data Distribution Challenges

Jia Wang, Yawen Li, Zhe Xue, Yingxia Shao, Zeli Guan, Wenling Li

Track: Social networks and social media

Keywords: federated heterogeneous graph learning, federated learning, heterogeneous information network

Abstract: Federated heterogeneous graph learning, an extension of federated learning, enables effective representation of complex multidimensional relationships while preserving data privacy. In horizontal federated heterogeneous graph learning, data from different parties often differ in topology and semantic distributions, causing sensitivity to distribution imbalance and amplifying the complexity of the topological structure. This interaction makes it difficult for models to learn shared representations, leading to increased instability during training. To address these challenges, this paper proposes a novel multi-scale adaptive horizontal federated heterogeneous graph learning method MAFedHGL. A random masking mechanism forces the model to infer missing connections. The model also captures multi-hop and multi-path connections using high-order topology mining, enhancing robustness against structural heterogeneity. Dynamic semantic consistency modeling uses a masking matrix to recover and integrate diverse node attributes, ensuring both global and local semantic consistency. Using clustering coefficients as aggregation weights enables clients with richer structural information to contribute more effectively to the global model, improving adaptability and performance across varying data distributions in horizontal federated heterogeneous graph learning. Extensive experiments on multiple public heterogeneous graph datasets validate that the proposed method outperforms state-of-the-art methods in both performance and robustness across various data distribution scenarios.

Submission Number: 2287