Distributed Federated Learning Framework for Cross-domain Risk Information Detection in Agricultural Product Supply Chains

Abstract:

The security of agricultural product supply chains plays a critical role in national development and social stability. However, the inherently complex structure of these supply chains—characterized by multiple stages, diverse stakeholders, and heterogeneous data sources—poses significant challenges for risk information sharing, especially in balancing data privacy protection with accurate risk detection. In response, a novel cross-domain risk information detection and trustworthy sharing model was proposed by integrating blockchain and federated learning technologies. Specifically, a distributed federated learning-based interaction framework was established to enable secure and decentralized circulation of risk information across different supply chain entities. To enhance anomaly detection, a multi-level evaluation mechanism based on the isolation forest algorithm was introduced to identify abnormal data patterns at various stages of the supply chain. Additionally, a dynamic risk contribution and credit evaluation model was developed to incentivize stakeholders to continuously share high-value risk data, while assessing their trustworthiness and participation levels in real time. Extensive experiments validated the effectiveness of the proposed approach in improving the efficiency, accuracy, and reliability of cross-domain risk information sharing. This work can provide a scalable and privacy-preserving solution tailored for the agricultural supply chain, offering practical implications for intelligent risk governance and data-driven decision-making in agri-food systems.