Cross-border logistics risk warning system based on federated learning
· Source: nature.com
In short
This article presents SafeLogFL, a federated learning-based risk warning system designed to enhance privacy and collaboration in cross-border logistics. SafeLogFL enables multiple logistics stakeholders to train machine learning models locally on sensitive data, sharing only model updates to predict risks like delays and disruptions without centralizing data. The system achieves 91.3% prediction accuracy while ensuring compliance with privacy regulations such as GDPR, demonstrating scalability and fostering secure international logistics collaboration.
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Tags: logistics, federated learning, risk, privacy, supply chain, collaboration