基于生成对抗网络的隐私增强联邦学习方案

doi:10.11959/j.issn.2096-109x.2023043

Abstract

Abstract:

Federated learning, a distributed machine learning paradigm, has gained a lot of attention due to its inherent privacy protection capability and heterogeneous collaboration.However, recent studies have revealed a potential privacy risk known as “gradient leakage”, where the gradients can be used to determine whether a data record with a specific property is included in another participant’s batch, thereby exposing the participant’s training data.Current privacy-enhanced federated learning methods may have drawbacks such as reduced accuracy, computational overhead, or new insecurity factors.To address this issue, a differential privacy-enhanced generative adversarial network model was proposed, which introduced an identifier into vanilla GAN, thus enabling the input data to be approached while satisfying differential privacy constraints.Then this model was applied to the federated learning framework, to improve the privacy protection capability without compromising model accuracy.The proposed method was verified through simulations under the client/server (C/S) federated learning architecture and was found to balance data privacy and practicality effectively compared with the DP-SGD method.Besides, the usability of the proposed model was theoretically analyzed under a peer-to-peer (P2P) architecture, and future research work was discussed.

Key words: federated learning, gradient leakage, privacy enhancement, generative adversarial network, differential privacy

CLC Number:

TP393

Feng YU, Qingxin LIN, Hui LIN, Xiaoding WANG. Privacy-enhanced federated learning scheme based on generative adversarial networks[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 113-122.

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References 26

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模型	分类准确率
模型	No	GAN	DP-SGD	DP-enabled GAN
MLP	97%	82%	60%	78%
CNN	99%	82%	63%	79%

模型	分类准确率
模型	No	GAN	DP-SGD	DP-enabled GAN
MLP	86%	75%	50%	65%
CNN	89%	72%	45%	64%

Privacy-enhanced federated learning scheme based on generative adversarial networks

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