中图分类号：TP181
文献标识码：A
DOI:10.19358/j.issn.2097-1788.2023.04.011
引用格式：邱浩宸,张信明.基于属性加密的联邦学习参与方选择方案［J］.网络安全与数据治理，2023，42（4）：62-69.

Client selection scheme for federated learning with attributebased encryption

Abstract： Federated learning protects privacy by enabling users to train models with local data, but existing works has generally ignored applicability in realworld scenarios. In view of the problem that traditional federated learning frameworks cannot avoid sharing models with inappropriate participants, an efficient federated learning participant selection scheme is proposed. A new ciphertextpolicy attribute based encryption with attribute revocation is designed to achieve efficient access control for participants without disclosing privacy. The security of the proposed scheme is discussed in depth, and the simulation experiment is carried out with open datesets. The results show that the scheme greatly improves the performance of the model while providing reliable participant selection applications, which can promote the widespread implementation of federated learning in smart cities and other scenarios.

Key words : attribute-based encryption; federated learning; attribute revocation