SEPP-FLBC: A Secure and Efficient Privacy Protection Scheme Using Federate Learning and Blockchain for Edge-End-Cloud Devices
文献类型：期刊论文
| 作者 | Feng, Libo2,4,5; Guo, Junwei2,4,5; Fang, Fake2,4,5; He, Zhenli2,4,5; Yu, Yimin1; Yao, Shaowen2,4,5; Peng, Xiaohui3 |
| 刊名 | IEEE TRANSACTIONS ON SERVICES COMPUTING
 |
| 出版日期 | 2026 |
| 卷号 | 19期号:1页码:657-670 |
| 关键词 | Training Federated learning Privacy Computational modeling Differential privacy Data models Convergence Protection Consensus protocol Servers Blockchain edge-end-cloud devices federated learning committee consensus differential privacy |
| ISSN号 | 1939-1374 |
| DOI | 10.1109/TSC.2025.3641964 |
| 英文摘要 | The convergence of federated learning (FL) and blockchain in edge-end-cloud systems offers promising opportunities for privacy-preserving collaborative intelligence. However, existing blockchain-enhanced FL (BFL) approaches remain vulnerable to malicious participants and lack robust protection for model updates. To address these issues, we propose SEPP-FLBC, a Secure and Efficient Privacy Protection framework based on Federated Learning and Blockchain Committees. SEPP-FLBC introduces a novel blockchain committee consensus mechanism to validate model updates and defend against unreliable nodes. It further employs a refined multi-party communication paradigm to facilitate indirect and secure data interactions, reducing the risk of information leakage. Additionally, differential privacy noise is applied to model updates to enhance resistance to inference attacks. A formal convergence analysis is conducted to ensure model stability and minimize overhead. Extensive experiments on benchmark datasets demonstrate that SEPP-FLBC achieves superior accuracy while maintaining strong privacy guarantees and communication efficiency, outperforming state-of-the-art BFL methods in both security and performance. |
| 资助项目 | National Natural Science Foundation of China[62362068] ; National Natural Science Foundation of China[62441214] ; National Natural Science Foundation of China[62462065] ; Yunnan Provincial Key Fields Science and Technology Program[202303AC100001] ; Yunnan Provincial Key Fields Science and Technology Program[202403AP140021] ; Basic Research Program of the Yunnan Provincial Department of Education[2025Y0044] ; Open Research Fund of the Yunnan Key Laboratory of Software Engineering[2023SE208] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001682677600035 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42803]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Peng, Xiaohui |
| 作者单位 | 1.Yunnan Univ Finance & Econ, Sch Informat, Kunming 650221, Peoples R China 2.Yunnan Univ, Engn Res Ctr Cyberspace, Kunming 650500, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Yunnan Univ, Yunnan Key Lab Software Engn, Kunming 650500, Peoples R China 5.Yunnan Univ, Sch Software, Kunming 650500, Peoples R China |
| 推荐引用方式 GB/T 7714 | Feng, Libo,Guo, Junwei,Fang, Fake,et al. SEPP-FLBC: A Secure and Efficient Privacy Protection Scheme Using Federate Learning and Blockchain for Edge-End-Cloud Devices[J]. IEEE TRANSACTIONS ON SERVICES COMPUTING,2026,19(1):657-670. |
| APA | Feng, Libo.,Guo, Junwei.,Fang, Fake.,He, Zhenli.,Yu, Yimin.,...&Peng, Xiaohui.(2026).SEPP-FLBC: A Secure and Efficient Privacy Protection Scheme Using Federate Learning and Blockchain for Edge-End-Cloud Devices.IEEE TRANSACTIONS ON SERVICES COMPUTING,19(1),657-670. |
| MLA | Feng, Libo,et al."SEPP-FLBC: A Secure and Efficient Privacy Protection Scheme Using Federate Learning and Blockchain for Edge-End-Cloud Devices".IEEE TRANSACTIONS ON SERVICES COMPUTING 19.1(2026):657-670. |
入库方式： OAI收割
来源：计算技术研究所
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