Edge-Driven Federated Learning System for Secure and Scalable Financial Data Sharing With Privacy Protection

INTRODUCTION: Cross-institutional collaborative financial risk modeling can improve fraud detection and anti-money laundering performance. However, data sensitivity, institutional isolation, non-IID data distributions, and heterogeneous edge nodes make traditional federated learning difficult in terms of privacy protection, communication efficiency, and robustness. OBJECTIVES: This paper aims to develop an edge-driven federated learning framework for secure cross-institutional financial risk modeling, enabling collaborative learning without sharing raw financial data. METHODS: The proposed framework integrates risk-relationship-aware dynamic grouping, privacy-constrained representation transmission, robust secure aggregation, and low-dimensional summary consistency checking. By limiting the scope of information exchange and controlling representation sharing, the framework reduces representation deviation and mitigates the influence of abnormal updates. RESULTS: Experiments were conducted on five public or synthetic financial risk datasets. The proposed method achieved the best federated AUPRC on four datasets and was only slightly lower than DSFL on the Elliptic dataset. Under client dropout and malicious-node settings, the method maintained relatively stable AUPRC performance and showed favorable communication efficiency and robustness. CONCLUSION: The results indicate that the proposed edge-driven federated learning framework can support privacy-preserving and robust cross-institutional financial risk modeling. It provides an effective solution for collaborative fraud detection and anti-money laundering under data isolation, heterogeneous edge environments, and adversarial conditions.