农业机械学报2025，Vol.56Issue(6)：56-66,89,12.DOI:10.6041/j.issn.1000-1298.2025.06.005
基于分布式联邦学习的农产品供应链跨域风险信息检测研究
Distributed Federated Learning Framework for Cross-domain Risk Information Detection in Agricultural Product Supply Chains
摘要
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.关键词
农产品供应链/风险信息检测/食品安全/分布式联邦学习/区块链Key words
agricultural supply chain/risk information identification/food safety/distributed federated learning/blockchain分类
农业科技引用本文复制引用
张新,肖柳君,许继平,于家斌,谭学泽,赵峙尧..基于分布式联邦学习的农产品供应链跨域风险信息检测研究[J].农业机械学报,2025,56(6):56-66,89,12.基金项目
国家重点研发计划项目(2022YFF1101103)、国家自然科学基金项目(62402020)和北京市教育委员会"市属高校分类发展——北京工商大学数字商学新兴交叉学科平台建设"项目 （2022YFF1101103）
