Privacy-Preserving Federated Heterogeneous Graph Learning via Pseudo-Metapath Generation
Hanrui Zhang, Yonghui Xu, Hanlin Zhang, Yibowen Zhao, Haotian Chen, Wei He, Lizhen Cui
Abstract
Federated heterogeneous graph learning helps users mine distributed heterogeneous data from consumer electronics while protecting data privacy. However, traditional federated graph learning methods often concentrate solely on aggregating model parameters, overlooking the global structure inherent in intricate heterogeneous graphs from various consumer electronics. This limitation hinders performance enhancement. In this context, where each client of consumer electronics institution holds a fragmentary local subgraph, there exist severed metapaths between these subgraphs. Restoring these missing connections could significantly boost performance. To reconstruct the cross-client global structure of heterogeneous subgraphs, while maintaining robust privacy protections, this paper introduces a novel approach: Pseudo-Metapath-based Federated learning framework for Heterogeneous Graph learning, dubbed PM-FedHG. Using metapaths as a guide, we’ve devised a relation-based technique to generate pseudo neighbor nodes and employ these pseudo metapaths for information exchange. Furthermore, our federated graph fusion and pseudo metapaths allocation algorithm facilitate the recovery of missing cross-client subgraph information, thereby enhancing performance through collaborative training. Crucially, as all uploaded metapaths are pseudo, the privacy of the original data remains securely protected. Comprehensive experiments on two datasets, across various client configurations, underscore the effectiveness of PM-FedHG. Additional ablation studies confirm the necessity and efficacy of each component within our framework.