PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning

Client heterogeneity creates both an opportunity and a risk in personalized federated graph learning. Knowledge held by other subgraphs may complement a receiver's Local model, but an incompatible transfer can override reliable predictions. One-shot communication sharpens this tension because an uns