山东理工大学学报（自然科学版）2026，Vol.40Issue(3)：50-58,9.
考虑隐私保护的在线单点反馈无投影去中心化联邦学习算法
Privacy-preserving online bandit projection-free decentralized federated learning algorithm
摘要
Abstract
This study investigates a decentralized federated learning(DFL)algorithm that considers client privacy protection,with the objective of safeguarding private information of each client from expo-sure while ensuring the model converges to the global optimal solution.First,a Frank-Wolfe projection-free decentralized federated learning algorithm based on differential privacy is proposed for the iterative process.The algorithm is integrated with online single-point feedback technology to avoid complex projec-tion calculations under high-dimensional constraint sets,and solves the problem of inaccessible gradient information by approximating gradients with function values.In scenarios without a centralized server,the algorithm achieves client privacy protection.Theoretical analysis demonstrates that the algorithm can con-verge to the global optimal solution.Finally,the effectiveness of the algorithm is verified through simula-tion experiments on a dataset.关键词
去中心化联邦学习/Frank-Wolfe/差分隐私/单点反馈Key words
decentralized federated learning/Frank-Wolfe/differential privacy/one-point bandit feed-back分类
信息技术与安全科学引用本文复制引用
王燕,邓志良,赵中原..考虑隐私保护的在线单点反馈无投影去中心化联邦学习算法[J].山东理工大学学报（自然科学版）,2026,40(3):50-58,9.基金项目
国家自然科学基金项目(U23B2061) （U23B2061）
