Cross-border logistics risk warning system based on federated learning

As international trade grows, managing cross-border logistics becomes more complicated and riskier. This paper aims to build a secure and private risk warning system that allows different logistics partners to collaborate without sharing sensitive information. Traditional systems that try to predict these risks collect all data in one place, creating privacy issues and making it hard for companies and governments in different regions to work together. This paper proposes a framework called the Secure and Federated Logistics Risk Warning System using Federated Learning (SafeLogFL). SafeLogFL is a privacy-preserving risk alert system for cross-border logistics that ensures secure, decentralized collaboration across different entities involved in logistics. Instead of sharing sensitive data, each participant trains the model locally on its data. The locally trained model updates are then aggregated using the Federated Averaging (Fed Avg) algorithm, which ensures model convergence while maintaining data privacy. Experimental results showed an average accuracy of 91.3% and compliance with privacy laws such as the General Data Protection Regulation (GDPR), proving the system's effectiveness in predicting delays, disruptions, and compliance issues. SafeLogFL provides a scalable, privacy-preserving solution for managing risks in global logistics, fostering secure collaboration between multiple parties.