BAFL-SVM: A blockchain-assisted federated learning-driven SVM framework for smart agriculture

Abstract

The combination of blockchain and Internet of Things technology has made significant progress in smart agriculture, which provides substantial support for data sharing and data privacy protection. Nevertheless, achieving efficient interactivity and privacy protection of agricultural data remains a crucial issues. To address the above problems, we propose a blockchain-assisted federated learning-driven support vector machine (BAFL-SVM) framework to realize efficient data sharing and privacy protection. The BAFL-SVM is composed of the FedSVM-RiceCare module and the FedPrivChain module. Specifically, in FedSVM-RiceCare, we utilize federated learning and SVM to train the model, improving the accuracy of the experiment. Then, in FedPrivChain, we adopt homomorphic encryption and a secret-sharing scheme to encrypt the local model parameters and upload them. Finally, we conduct a large number of experiments on a real-world dataset of rice pests and diseases, and the experimental results show that our framework not only guarantees the secure sharing of data but also achieves a higher recognition accuracy compared with other schemes.

Keywords

Smart agriculture / Blockchain / Federated learning / Privacy protection

CRediT authorship contribution statement

Ruiyao Shen: Methodology, Validation, Formal analysis, Writing - original draft. Hongliang Zhang: Conceptualization, Methodology. Baobao Chai: Investigation, Resources. Wenyue Wang: Visualization, Supervision. Guijuan Wang: Supervision, Funding acquisition. Biwei Yan: Writing - review & editing. Jiguo Yu: Writing - review & editing, Supervision, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (62272256, 62202250), the Major Program of Shandong Provincial Natural Science Foundation for the Fundamental Research (ZR2022ZD03), the National Science Foundation of Shandong Province (ZR2021QF079), the Talent Cultivation Promotion Program of Computer Science and Technology in Qilu University of Technology (Shandong Academy of Sciences) (2023PY059), the Pilot Project for Integrated Innovation of Science, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) (2022XD001), and the Colleges and Universities 20 Terms Foundation of Jinan City (202228093).

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