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[Objective]The flow of private data elements plays a crucial role in the food supply chain, and the safe and efficient operation of the food supply chain can be ensured through the effective management and flow of private data elements. Through collaborative computing among the whole chain of the food supply chain, the production, transportation and storage processes of food can be better monitored and managed, so that possible quality and safety problems can be detected and solved in a timely manner, and the health and rights of consumers can be safeguarded. It can also be applied to the security risk assessment and early warning of the food supply chain. By analyzing big data, potential risk factors and abnormalities can be identified, and timely measures can be taken for early warning and intervention to reduce the possibility of quality and safety risks. This study combined the industrial Internet identification and resolution system with the federated learning algorithm, which can realize collaborative learning among multiple enterprises, and each enterprise can carry out collaborative training of the model without sharing the original data, which protects the privacy and security of the data while realizing the flow of the data, and it can also make use of the data resources distributed in different segments, which can realize more comprehensive and accurate collaborative calculations, and improve the safety and credibility of the industrial Internet system's security and credibility.[Methods]To address the problem of not being able to share and participate in collaborative computation among different subjects in the grain supply chain due to the privacy of data elements, this study first analyzed and summarized the characteristics of data elements in the whole link of grain supply chain, and proposed a grain supply chain data flow and collaborative computation architecture based on the combination of the industrial Internet mark resolution technology and the idea of federated learning, which was constructed in a layered and graded model to provide a good infrastructure for the decentralized between the participants. The data identification code for the flow of food supplied chain data elements and the task identification code for collaborative calculation of food supply chain, as well as the corresponding parameter data model, information data model and evaluation data model, were designed to support the interoperability of federated learning data. A single-link horizontal federation learning model with isomorphic data characteristics of different subjects and a cross-link vertical federation learning model with heterogeneous data characteristics were constructed, and the model parameters were quickly adjusted and calculated based on logistic regression algorithm, neural network algorithm and other algorithms, and the food supply chain security risk assessment scenario was taken as the object of the research, and the research was based on the open source FATE (Federated AI Technology) federation learning model. Enabler (Federated AI Technology) federated learning platform for testing and validation, and visualization of the results to provide effective support for the security management of the grain supply chain.[Results and Discussion]Compared with the traditional single-subject assessment calculation method, the accuracy of single-session isomorphic horizontal federation learning model assessment across subjects was improved by 6.7%, and the accuracy of heterogeneous vertical federation learning model assessment across sessions and subjects was improved by 8.3%. This result showed that the single-session isomorphic horizontal federated learning model assessment across subjects could make full use of the data information of each subject by merging and training the data of different subjects in the same session, thus improving the accuracy of security risk assessment. The heterogeneous vertical federated learning model assessment of cross-session and cross-subject further promotes the application scope of collaborative computing by jointly training data from different sessions and subjects, which made the results of safety risk assessment more comprehensive and accurate. The advantage of combining federated learning and logo resolution technology was that it could conduct model training without sharing the original data, which protected data privacy and security. At the same time, it could also realize the effective use of data resources and collaborative computation, improving the efficiency and accuracy of the assessment process.[Conclusions]The feasibility and effectiveness of this study in practical applications in the grain industry were confirmed by the test validation of the open-source FATE federated learning platform. This provides reliable technical support for the digital transformation of the grain industry and the security management of the grain supply chain, and helps to improve the intelligence level and competitiveness of the whole grain industry. Therefore, this study can provide a strong technical guarantee for realizing the safe, efficient and sustainable development of the grain supply chain. |
