信阳师范学院学报（自然科学版）2025，Vol.38Issue(1)：66-71,6.DOI:10.3969/j.issn.2097-583X.2025.01.009
一种基于"云-边"协同计算的新安全联邦学习方案
A novel federated learning scheme based on cloud-edge collaborative computing
摘要
Abstract
In the process of federated learning,how to efficiently protect the privacy and the integrity of local and global training models is an urgent problem to be solved. Traditional secure federated learning methods based on differential privacy have shortcomings such as high computational overhead,high communication energy consumption and long execution time. Therefore,a novel secure and efficient federated learning scheme (SEFL) based on "cloud edge" collaborative computing was proposed. SEFL ensured the security of model aggregation by configuring the Intel SGX-based TEE (Trusted Execution Environment) on the Cloud Server (CS). It combined the symmetric and the asymmetric encryption technologies to protect the communication security between CS and Edge Servers (ESs),and improved the security of model storage by constructing a chained storage structure on ESs. Theoretical analyses and experimental results showed that SEFL could secure FL and effectively improve the FL training efficiency.关键词
联邦学习/可信执行环境/链式存储结构/隐私性/完整性Key words
federated learning(FL)/trusted execution environment(TEE)/chained storage structure/privacy/integrity分类
计算机与自动化引用本文复制引用
马行坡,闫梦凡,闵洁,殷明..一种基于"云-边"协同计算的新安全联邦学习方案[J].信阳师范学院学报（自然科学版）,2025,38(1):66-71,6.基金项目
国家自然科学基金项目(61702438) （61702438）
河南省高等学校重点科研项目(23A520021) （23A520021）
信阳农林学院青年教师科研基金资助项目(QN2022031) （QN2022031）
信阳师范学院2022年研究生科研创新基金项目(2022KYJJ016) （2022KYJJ016）
