HarmoFGL: Harmonizing GNN Latent Factors for Federated Graph Learning

Yeyu Yan, Zhenfeng Zhu, Shuai Zheng, Hongli Xu, Yawei Zhao, Kunlun He, Yao Zhao

Abstract: Federated graph learning (FGL), as a privacy-preserving paradigm for distributed graph data training, aims to resolve graph data isolation issues under the framework of federated learning (FL). Despite the significant efforts made by existing FGL methods, two key challenges are still not well addressed: 1) how to mitigate graph heterogeneity in clients arising from feature deviation and structural deviation and 2) how to devise a favorable aggregation mechanism to maximize the client’s benefit from collaborative training with privacy preserving. To tackle these issues, we take a perspective of latent factor and propose a HarmoFGL framework by Harmonizing graph neural network (GNN) latent factors for Federated Graph Learning, achieving cross-client federated training by coordinating personalized aggregation and client-level representation in a symbiotic space. To alleviate feature deviation, an implicit feature crossing (IFC) approach is proposed through the disentanglement of higher order feature dependency into client-universal and client-specific interactions. As for the graph heterogeneity induced by structural deviation, we establish a cross-client symbiotic parameter space spanned by GNN latent factors, on which a client-level representation is derived to characterize the inherent properties of clients. On the server side, on the basis of client relevance-driven personalized parameter aggregation, graph Laplacian regularization on client-level representations is implemented for collaborative training. Experimental results on five public graph datasets and two medical datasets demonstrate the effectiveness of HarmoFGL.

External IDs:doi:10.1109/tnnls.2025.3648828