BAFL-SVM: A blockchain-assisted federated learning-driven SVM framework for smart agriculture
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 c...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | High-Confidence Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667295224000461 |
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| author | Ruiyao Shen Hongliang Zhang Baobao Chai Wenyue Wang Guijuan Wang Biwei Yan Jiguo Yu |
| author_facet | Ruiyao Shen Hongliang Zhang Baobao Chai Wenyue Wang Guijuan Wang Biwei Yan Jiguo Yu |
| author_sort | Ruiyao Shen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a66c6eb9d87c4e0a82c09f42c99e2340 |
| institution | Kabale University |
| issn | 2667-2952 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | High-Confidence Computing |
| spelling | doaj-art-a66c6eb9d87c4e0a82c09f42c99e23402025-08-20T03:44:28ZengElsevierHigh-Confidence Computing2667-29522025-03-015110024310.1016/j.hcc.2024.100243BAFL-SVM: A blockchain-assisted federated learning-driven SVM framework for smart agricultureRuiyao Shen0Hongliang Zhang1Baobao Chai2Wenyue Wang3Guijuan Wang4Biwei Yan5Jiguo Yu6School of Computer Science and Technology, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science and Technology, Qilu University of Technology, Jinan 250353, ChinaSchool of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Computer Science and Technology, Qufu Normal University, Rizhao 276826, ChinaKey Laboratory of Computing Power Network and Information Security Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China; Shandong Provincial Key Laboratory of Computer Networks, Jinan 250098, China; Corresponding author.School of Computer Science and Technology, Shandong University, Qingdao 266237, ChinaBig Data Institute, Qilu University of Technology, Jinan 250353, ChinaThe 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.http://www.sciencedirect.com/science/article/pii/S2667295224000461Smart agricultureBlockchainFederated learningPrivacy protection |
| spellingShingle | Ruiyao Shen Hongliang Zhang Baobao Chai Wenyue Wang Guijuan Wang Biwei Yan Jiguo Yu BAFL-SVM: A blockchain-assisted federated learning-driven SVM framework for smart agriculture High-Confidence Computing Smart agriculture Blockchain Federated learning Privacy protection |
| title | BAFL-SVM: A blockchain-assisted federated learning-driven SVM framework for smart agriculture |
| title_full | BAFL-SVM: A blockchain-assisted federated learning-driven SVM framework for smart agriculture |
| title_fullStr | BAFL-SVM: A blockchain-assisted federated learning-driven SVM framework for smart agriculture |
| title_full_unstemmed | BAFL-SVM: A blockchain-assisted federated learning-driven SVM framework for smart agriculture |
| title_short | BAFL-SVM: A blockchain-assisted federated learning-driven SVM framework for smart agriculture |
| title_sort | bafl svm a blockchain assisted federated learning driven svm framework for smart agriculture |
| topic | Smart agriculture Blockchain Federated learning Privacy protection |
| url | http://www.sciencedirect.com/science/article/pii/S2667295224000461 |
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