When Sharing Hurts: Risk-Reversible Knowledge Transfer for Federated Graph Learning

Cross-client graph transfer changes both a client’s predictor and the representations used to construct later shared evidence. Harmful updates may therefore persist across rounds even after transfer is downweighted. We introduce Risk-Reversible Graph Transfer (R2GT), a federated graph learning framework that makes the retention of transfer-influenced states client-specific and reversible. R2GT combines semantic and structural sharing with a validation-risk gate, a recoverable checkpoint, and cooldown-based re-entry. After warm-up and cooldown, each client attempts transfer, retains the candidate if its validation score remains within a tolerance of the stored reference, and otherwise restores that reference. We establish trajectory-wise empirical validation control, extend it to population risk under explicit concentration and validation–test coupling conditions, and characterize how rollback differs from loss masking at revocation. Across homophilic and heterophilic graphs with disjoint and overlapping partitions, R2GT improves average performance over FedSSA in all four evaluation groups. Paired rollback analyses further show that recovery improves next-round performance in 86.0–91.4% of rollback events and reduces semantic and structural drift relative to mask-only continuation. Together, these results link checkpoint restoration to subsequent client performance and to the stability of the evidence shared in later rounds.