面向用户的支持用户掉线的联邦学习数据隐私保护方法

联邦学习是解决多组织协同训练问题的一种有效手段,但是现有的联邦学习存在不支持用户掉线、模型API泄露敏感信息等问题。文章提出一种面向用户的支持用户掉线的联邦学习数据隐私保护方法,可以在用户掉线和保护的模型参数下训练出一个差分隐私扰动模型。该方法利用联邦学习框架设计了基于深度学习的数据隐私保护模型,主要包含两个执行协议:服务器和用户执行协议。用户在本地训练一个深度模型,在本地模型参数上添加差分隐私扰动,在聚合的参数上添加掉线用户的噪声和,使得联邦学习过程满足(ε,δ)-差分隐私。实验表明,当用户数为50、ε=1时,可以在模型隐私性与可用性之间达到平衡。

User-oriented Data Privacy Preserving Method for Federated Learning that Supports User Disconnection

Federated learning is an effective method to solve the problem of multiorganization collaborative training.However,existing federated learning has problems such as not supporting user disconnection and model API leaking sensitive information.This paper proposes a user-oriented federated learning data privacy preserving method that supports user disconnection,which can train a differential privacy disturbance model under user disconnection and protected model parameters.This paper uses a federated learning framework to design a data privacy preserving model based on deep learning.It mainly contains two execution protocols,server and user execution protocol.User trains a deep model locally, adds differential privacy disturbance to the local model parameters, andadds sum noise of dropped users to the aggregated parameters so that the federated learningprocess meets (ε,δ)-differential privacy. Experiments show that when the number of users is50 and ε=1, a balance can be reached between model privacy and usability.