中圖分類號(hào)： TN711.1,；TP311 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211828 中文引用格式： 楊東寧，謝瀟睿,，吉志坤,，等. 一種隱私保護(hù)的聯(lián)邦學(xué)習(xí)框架[J].電子技術(shù)應(yīng)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
Yang Dongning1,，2，Xie Xiaorui1，Ji Zhikun3,，Ji Weiwei3
1.Information Center,，Yunnan Power Grid Co.，Ltd.,，Kunming 650011,，China； 2.School of Big Data and Intelligent Engineering,，Southwest Forestry University,，Kunming 650224，China,； 3.Yunnan Yundian Tongfang Technology Co.,，Ltd.，Kunming 650220,，China
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
