中图分类号：TN711.1；TP311
文献标识码：A
DOI：10.16157/j.issn.0258-7998.211828
中文引用格式：杨东宁，谢潇睿，吉志坤，等. 一种隐私保护的联邦学习框架[J].电子技术应用，2022，48(5)：94-97，103.
英文引用格式：Yang Dongning，Xie Xiaorui，Ji Zhikun，et al. A privacy-preserving federated learning framework[J]. Application of Electronic Technique，2022，48(5)：94-97，103.

A privacy-preserving federated learning framework

Abstract：In the era of big data, more and more attention has been paid to data security and privacy. Federated learning is regarded as a promising privacy-preserving technology, which allows training a deep model from decentralized data. To solve the problem of isolated data island and privacy protection caused by the fear of data privacy information leakage in the power investment system，this paper proposes a privacy-preserving federal learning framework, which allows departments to jointly train the model without releasing their local data. Firstly, a federated learning architecture is proposed to support distributed training model. Secondly, homomorphic encryption technology is introduced and a federal average learning process is proposed to realize the joint training model while the privacy of data are protected. Finally, the experimental results show that the framework has good convergence and the joint training model has good accuracy.

Key words :data privacy；federated learning；deep learning；homomorphic encryption；convolutional neural network