A Blockchain-based federated learning framework for secure aggregation and fair incentives
Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that in...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Connection Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2024.2316018 |
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| author | XiaoHui Yang TianChang Li |
| author_facet | XiaoHui Yang TianChang Li |
| author_sort | XiaoHui Yang |
| collection | DOAJ |
| description | Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that integrates federated learning with blockchain technology to facilitate secure model aggregation and fair incentives in untrustworthy environments. The framework designs a reputation evaluation method using quality as an indicator, and a consensus method based on reputation feedback. The trustworthiness of nodes is dynamically assessed to achieve an efficient and trustworthy model aggregation process while avoiding reputation monopolisation. Furthermore, the paper defines a tailored contribution calculation process for nodes in different roles in an untrusted environment. A reward and punishment scheme based on the joint constraints of contribution and reputation is proposed to attract highly qualified workers to actively participate in federated learning tasks. Theoretical analysis and simulation experiments demonstrate the framework's ability to maintain efficient and secure aggregation under a certain degree of attack while achieving fair incentives for each role node with significantly reduced consensus consumption. |
| format | Article |
| id | doaj-art-0c8fba7c2ec34daaacfbd447edd02061 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-0c8fba7c2ec34daaacfbd447edd020612025-08-20T01:59:22ZengTaylor & Francis GroupConnection Science0954-00911360-04942024-12-0136110.1080/09540091.2024.2316018A Blockchain-based federated learning framework for secure aggregation and fair incentivesXiaoHui Yang0TianChang Li1School of Cyber Security and Computer, Hebei University, Baoding, People’s Republic of ChinaSchool of Cyber Security and Computer, Hebei University, Baoding, People’s Republic of ChinaFederated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that integrates federated learning with blockchain technology to facilitate secure model aggregation and fair incentives in untrustworthy environments. The framework designs a reputation evaluation method using quality as an indicator, and a consensus method based on reputation feedback. The trustworthiness of nodes is dynamically assessed to achieve an efficient and trustworthy model aggregation process while avoiding reputation monopolisation. Furthermore, the paper defines a tailored contribution calculation process for nodes in different roles in an untrusted environment. A reward and punishment scheme based on the joint constraints of contribution and reputation is proposed to attract highly qualified workers to actively participate in federated learning tasks. Theoretical analysis and simulation experiments demonstrate the framework's ability to maintain efficient and secure aggregation under a certain degree of attack while achieving fair incentives for each role node with significantly reduced consensus consumption.https://www.tandfonline.com/doi/10.1080/09540091.2024.2316018Federated learningblockchainreputation mechanismconsensuspoisoning attack |
| spellingShingle | XiaoHui Yang TianChang Li A Blockchain-based federated learning framework for secure aggregation and fair incentives Connection Science Federated learning blockchain reputation mechanism consensus poisoning attack |
| title | A Blockchain-based federated learning framework for secure aggregation and fair incentives |
| title_full | A Blockchain-based federated learning framework for secure aggregation and fair incentives |
| title_fullStr | A Blockchain-based federated learning framework for secure aggregation and fair incentives |
| title_full_unstemmed | A Blockchain-based federated learning framework for secure aggregation and fair incentives |
| title_short | A Blockchain-based federated learning framework for secure aggregation and fair incentives |
| title_sort | blockchain based federated learning framework for secure aggregation and fair incentives |
| topic | Federated learning blockchain reputation mechanism consensus poisoning attack |
| url | https://www.tandfonline.com/doi/10.1080/09540091.2024.2316018 |
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