Disagreement-Aware Subgraph Federated Learning via Uncertainty-Guided Local-Global Alignment

Abstract

Subgraph federated learning (subgraph FL) enables collaborative graph neural network training without sharing raw graph data, but suffers from severe Non-IID distributions and structural fragmentation. In such settings, Non-IID distributions induce pronounced client specialization, where each client excels in a subset of nodes but remains insufficiently trained on others. However, existing similarity-based aggregation and distillation methods fail to balance client specialization and global generalization, often reinforcing dominant local representations and impairing generalization on under-represented nodes, or over-incorporating global knowledge and thereby disrupting well-optimized local specialization. In this paper, we propose FedDUA, a novel disagreement-aware and uncertainty-guided framework for subgraph FL. Specifically, FedDUA first models cross-client semantic disagreement via a lightweight semantic anchor graph and derives adaptive aggregation weights for reliable global federated knowledge. On the client side, FedDUA introduces an uncertainty-aware local-global semantic alignment mechanism that selectively reinforces representations of confident nodes while guiding uncertain nodes with aggregated global knowledge, thereby balancing local specialization and global generalization. Extensive experiments on six real-world datasets demonstrate that FedDUA consistently outperforms the state-of-the-art subgraph FL methods across varying numbers of clients. Further analyses validate the robustness and effectiveness of the proposed disagreement modeling and uncertainty-aware local-global semantic alignment strategies.