Who Knows What? One-Shot Federated Graph Learning via Diffusion

One-shot federated graph learning is well suited to communication-constrained environments because each client communicates with the server only once. However, this setting is particularly challenging when clients hold graphs that differ in their node features, label distributions, connectivity patterns, and local topology. Conventional aggregation treats each client uniformly across all prediction classes, overlooking variations in class-specific expertise caused by heterogeneous local data and graph structure. We propose CDRA, a lightweight Class-wise Diffusion Reliability Aggregation framework for one-shot federated graph learning under severe client heterogeneity. Each client trains a local graph neural network and constructs compact class-conditioned diffusion signatures that capture label-specific structural patterns within its graph. The server uses Hellinger distance to compare these signatures with federation-level class references and estimate each client’s reliability for different prediction classes. These reliability estimates, together with client size and class support, determine class-wise aggregation weights for combining local model predictions. Experiments on seven graph benchmarks under severe non-IID heterogeneity show that CDRA improves predictive performance while substantially reducing runtime, peak GPU memory, and upload communication relative to reconstruction-based one-shot federated graph learning methods. Anonymous code is available at: https://anonymous.4open.science/r/CDRA/README.md