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Related Experiment Video

Updated: Jan 12, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

Cross-border logistics risk warning system based on federated learning.

Xinwen Liang1

  • 1Logistics Management, School of Business Administration Group, Shanxi Finance and Economics University, Taiyuan, 030006, Shanxi, China. 17200677598@163.com.

Scientific Reports
|November 7, 2025
PubMed
Summary

This study introduces a Secure and Federated Logistics Risk Warning System (SafeLogFL) for global trade. It enables secure, private collaboration among logistics partners, enhancing risk prediction and compliance.

Keywords:
Cross-Border logisticsData privacyFederated learningMulti-Layer perceptronRisk prediction

Related Experiment Videos

Last Updated: Jan 12, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

Area of Science:

  • Logistics and Supply Chain Management
  • Information Security
  • Artificial Intelligence

Background:

  • Cross-border logistics face increasing complexity and risks.
  • Traditional centralized risk prediction systems raise privacy concerns and hinder inter-regional collaboration.
  • Need for a secure, decentralized system for global logistics risk management.

Purpose of the Study:

  • To develop a privacy-preserving risk warning system for cross-border logistics.
  • To enable secure, decentralized collaboration among logistics partners without sensitive data sharing.
  • To enhance the prediction of delays, disruptions, and compliance issues in international trade.

Main Methods:

  • Proposed a framework named Secure and Federated Logistics Risk Warning System using Federated Learning (SafeLogFL).
  • Utilized Federated Learning for decentralized model training on local data.
  • Employed the Federated Averaging (Fed Avg) algorithm for aggregating model updates securely.

Main Results:

  • Achieved an average accuracy of 91.3% in predicting logistics risks.
  • Demonstrated compliance with privacy regulations like the General Data Protection Regulation (GDPR).
  • Validated the system's effectiveness in identifying potential delays, disruptions, and compliance failures.

Conclusions:

  • SafeLogFL offers a scalable, privacy-preserving solution for global logistics risk management.
  • The system fosters secure collaboration between multiple logistics entities.
  • Decentralized approach enhances data privacy while maintaining high prediction accuracy.