Financial fraud detection across institutions faces a fundamental tension between the need for diverse training data and regulatory prohibitions on sharing sensitive records. Existing federated learning approaches suffer from performance degradation under non-IID distributions and substantial utility losses when uniform differential privacy is applied to inherently sparse fraud signals. To this end, this paper proposes HiFraud, a hierarchical federated framework featuring three key components: fraud-aware dynamic clustering with complementarity regularization to group institutions by fraud pattern similarity while preserving rare-type representation; star-chain knowledge transfer augmented by not-true-class distillation to propagate novel fraud patterns rapidly within clusters while mitigating catastrophic forgetting; and privacy-adaptive aggregation via Rényi differential privacy composition, calibrating noise intensity to distributional divergence and fraud rarity. Experiments on IEEE-CIS, PaySim, and Worldline datasets show that HiFraud achieves an area under the receiver operating characteristic curve (AUC-ROC) of 0.935 under ε=2.3, outperforming DP-FedAvg by 10.5% while reducing convergence from 49 to 30 rounds. The framework also suppresses membership inference attack success to 10.2%, detects emerging fraud patterns within 3 h inside clusters, and improves rare fraud type detection by 23.0% over uniform privacy baselines. These results demonstrate that hierarchical architectures can effectively reconcile detection performance, formal privacy guarantees, and rapid threat response in collaborative fraud detection.
Zhang et al. (Thu,) studied this question.