Federated Learning Framework for Cross-Regional Electronic Toll Fraud Detection in Connected Transportation Networks

Abstract

This research introduces a federated learning framework specifically designed for cross-regional electronic toll fraud detection that enables collaborative fraud prevention across different transportation authorities while maintaining data privacy and regional autonomy. The proposed Cross-Regional Toll Fraud Detection System (CRTFDS) addresses sophisticated fraud schemes that operate across multiple toll networks, exploiting differences in detection capabilities and information sharing limitations between regional operators. Our methodology employs federated machine learning to create shared fraud detection models that benefit from collective intelligence without requiring toll operators to share sensitive customer data, transaction records, or proprietary detection techniques. The system combines transaction pattern analysis, vehicle movement tracking, and payment behavior modeling to identify complex fraud schemes including transponder cloning, account sharing, and coordinated toll evasion. We introduce a novel privacy-preserving model aggregation protocol that enables toll authorities to contribute to and benefit from collaborative fraud detection while maintaining competitive confidentiality. The federated component continuously updates fraud detection models based on emerging fraud patterns observed across different regions, providing early warning capabilities for new fraud techniques. Our implementation includes differential privacy mechanisms, secure multi-party computation protocols, and automated fraud alert systems. Experimental validation across multiple regional toll networks demonstrates 84% improvement in fraud detection accuracy for cross-regional schemes while maintaining strict privacy requirements and reducing overall fraud losses by 61%.