FedPRE: Robust Federated Graph Learning against Topological Corruption

Federated Graph Learning (FGL) has emerged as a compelling paradigm for distributed Graph Neural Networks (GNNs) training, prioritizing data privacy preservation. However, due to the limitations of data collection and storage conditions, FGL suffers from data corruption in real-world applications. While Federated Learning (FL) and FGL studies have addressed label corruption, the challenge of graph topological corruption remains unexamined. Specifically, this phenomenon significantly disrupts node connectivity patterns of graphs, leading GNNs to adopt flawed feature propagation paradigms. Existing methods with poor robustness are inevitably constrained due to the absence of targeted strategies for addressing the issues of global contaminated collaboration and local vulnerability. To tackle this challenge, we conduct the first comprehensive investigation of robust FGL against topological corruption and propose FedPRE. It comprises: (1) Feature Propagation Robustness Evaluation (FPRE), which evaluates client GNNs feature propagation robustness and adjusts their contribution during aggregation. (2) Topological Corruption-Resistant Enhancement (TCRE), which enhances robustness against corruption during local training. Extensive experiments validate the robustness and effectiveness of FedPRE against topological corruption. The code is available at https://github.com/OakleyTan/FedPRE.