FedRog: Robust Federated Graph Classification for Strong Heterogeneity and High-Noise Scenarios

Federated graph classification has emerged as a promising paradigm for privacy-preserving graph learning across distributed clients. However, real-world federated scenarios often suffer from severe data heterogeneity and label noise, which significantly degrade model performance. To address these challenges, we propose FedRog, a robust and personalized federated graph neural network framework that improves generalization under non-IID and noisy label settings. FedRog introduces a parameter-aware selection and fine-tuning mechanism to align global and local representations, and a neighbor embedding consistency constraint to enhance robustness against noisy supervision. Furthermore, a fine-grained, importance-guided global aggregation strategy based on Fisher information is employed to mitigate unreliable updates from low-quality clients. We conduct extensive experiments on 16 graph classification datasets under five heterogeneous data partition settings. Results show that FedRog consistently achieves competitive or superior performance compared to 14 baselines in terms of both accuracy and robustness under clean and noisy conditions.