支持多数不规则用户的隐私保护联邦学习框架

陈前昕; 毕仁万; 林劼; 金彪; 熊金波

doi:10.11959/j.issn.2096-109x.2022011

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支持多数不规则用户的隐私保护联邦学习框架

学术论文 | 更新时间：2024-06-03

- 支持多数不规则用户的隐私保护联邦学习框架
- Privacy-preserving federated learning framework with irregular-majority users
- 网络与信息安全学报 2022年8卷第1期页码：139-150
- 作者机构：
  
  1. 福建师范大学计算机与网络空间安全学院，福建福州350117
  2. 福建师范大学福建省网络安全与密码技术重点实验室，福建福州 350007
- 作者简介：
  
  [ "陈前昕（1996− ），男，福建泉州人，福建师范大学硕士生，主要研究方向为安全深度学习、隐私保护技术" ]
  [ "毕仁万（1996− ），男，湖南常德人，福建师范大学博士生，主要研究方向为安全深度学习、安全多方计算" ]
  [ "林劼（1972− ），男，福建三明人，博士，福建师范大学教授，主要研究方向为机器学习、生物信息学" ]
  [ "金彪（1985− ），男，安徽六安人，博士，福建师范大学高级实验师，主要研究方向为信息安全、数据挖掘" ]
  [ "熊金波（1981−），男，湖南益阳人，博士，福建师范大学教授，主要研究方向为安全深度学习、移动群智感知、隐私保护技术" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61872088);国家自然科学基金资助项目;The National Natural Science Foundation of China(61872090);国家自然科学基金;The National Natural Science Foundation of China(U1905211);福建省自然科学基金资助项目;The Natural Science Foundation of Fujian Province(2019J01276)
- DOI：10.11959/j.issn.2096-109x.2022011
  中图分类号： TP309
- 网络首发：2022-02，
  
  纸质出版：2022-02-15
- 稿件说明：
移动端阅览
陈前昕, 毕仁万, 林劼, 等. 支持多数不规则用户的隐私保护联邦学习框架[J]. 网络与信息安全学报, 2022,8(1):139-150.

Qianxin CHEN, Renwan BI, Jie LIN, et al. Privacy-preserving federated learning framework with irregular-majority users[J]. Chinese Journal of Network and Information Security, 2022, 8(1): 139-150.
陈前昕, 毕仁万, 林劼, 等. 支持多数不规则用户的隐私保护联邦学习框架[J]. 网络与信息安全学报, 2022,8(1):139-150. DOI： 10.11959/j.issn.2096-109x.2022011.

Qianxin CHEN, Renwan BI, Jie LIN, et al. Privacy-preserving federated learning framework with irregular-majority users[J]. Chinese Journal of Network and Information Security, 2022, 8(1): 139-150. DOI： 10.11959/j.issn.2096-109x.2022011.

摘要

针对联邦学习存在处理大多数不规则用户易引起聚合效率降低，以及采用明文通信导致参数隐私泄露的问题，基于设计的安全除法协议构建针对不规则用户鲁棒的隐私保护联邦学习框架。该框架通过将模型相关计算外包给两台边缘服务器以减小采用同态加密产生的高额计算开销，不仅允许模型及其相关信息以密文形式在边缘服务器上进行密文聚合，还支持用户在本地进行模型可靠性计算以减小传统方法采用安全乘法协议造成的额外通信开销。在该框架的基础上，为更精准评估模型的泛化性能，用户完成本地模型参数更新后，利用边缘服务器下发的验证集与本地持有的验证集联合计算模型损失值，并结合损失值历史信息动态更新模型可靠性以作为模型权重。进一步，在模型可靠性先验知识指导下进行模型权重缩放，将密文模型与密文权重信息交由边缘服务器对全局模型参数进行聚合更新，保证全局模型变化主要由高质量数据用户贡献，提高收敛速度。通过HybridArgument模型进行安全性分析，论证表明PPRFL（privacy-preserving robust federated learning）可以有效保护模型参数以及包括用户可靠性等中间交互参数的隐私。实验结果表明，当联邦聚合任务中的所有参与方均为不规则用户时，PPRFL 方案准确率仍然能达到 92%，收敛效率较 PPFDL （privacy-preserving federated deep learning with irregular users）提高1.4倍；当联邦聚合任务中80%用户持有的训练数据都为噪声数据时，PPRFL方案准确率仍然能达到89%，收敛效率较PPFDL提高2.3倍。

Abstract

In response to the existing problems that the federated learning might lead to the reduction of aggregation efficiency by handing the majority of irregular users and the leak of parameter privacy by adopting plaintext communication

a framework of privacy-preserving robust federated learning was proposed for ensuring the robustness of the irregular user based on the designed security division protocol.PPRFL could enable the model and its related information to aggregate in ciphertext on the edge server facilitate users to calculate the model reliability locally for reducing the additional communication overhead caused by the adoption of the security multiplication protocol in conventional methods

apart from lowering the high computational overhead resulted from homomorphic encryption with outsourcing computing to two edge servers.Based on this

user could calculate the loss value of the model through jointly using the verification sets issued by the edge server and that held locally after parameter updating of the local model.Then the model reliability could be dynamically updated as the model weight together with the historic information of the loss value.Further

the model weight was scaled under the guidance of prior knowledge

and the ciphertext model and ciphertext weight information are sent to the edge server to aggregate and update the global model parameters

ensuring that global model changes are contributed by high-quality data users

and improving the convergence speed.Through the security analysis of the Hybrid Argument model

the demonstration shows that PPRFL can effectively protect the privacy of model parameters and intermediate interaction parameters including user reliability.The experimental results show that the PPRFL scheme could still achieve the accuracy of 92% when all the participants in the federated aggregation task are irregular users

with the convergence efficiency 1.4 times higher than that of the PPFDL.Besides

the PPRFL scheme could still reach the accuracy of 89% when training data possessed by 80% of the users in the federated aggregation task were noise data

with the convergence efficiency 2.3 times higher than that of the PPFDL.

关键词

Keywords

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