SEPP-FLBC: A Secure and Efficient Privacy Protection Scheme Using Federate Learning and Blockchain for Edge-End-Cloud Devices

doi:10.1109/TSC.2025.3641964

CSpace

	SEPP-FLBC: A Secure and Efficient Privacy Protection Scheme Using Federate Learning and Blockchain for Edge-End-Cloud Devices
	Feng, Libo 1,2,3; Guo, Junwei 1,2,3; Fang, Fake 1,2,3; He, Zhenli 1,2,3; Yu, Yimin 4; Yao, Shaowen 1,2,3; Peng, Xiaohui 5
	2026
发表期刊	IEEE TRANSACTIONS ON SERVICES COMPUTING
ISSN	1939-1374
卷号	19 期号:1 页码:657-670
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.
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
DOI	10.1109/TSC.2025.3641964
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering
WOS记录号	WOS:001682677600035
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42804
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, Engn Res Ctr Cyberspace, Kunming 650500, Peoples R China 2.Yunnan Univ, Yunnan Key Lab Software Engn, Kunming 650500, Peoples R China 3.Yunnan Univ, Sch Software, Kunming 650500, Peoples R China 4.Yunnan Univ Finance & Econ, Sch Informat, Kunming 650221, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, 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.