Research on Centralized Differential Privacy Algorithm for Federated Learning

doi:10.3969/j.issn.1671-1122.2024.01.007

Abstract

Abstract:

Federated learning has received increasing attention in recent years for breaking down “data silos” with unique training methods. However, when the global model is trained, federation learning is vulnerable to inference attacks, which may reveal the information of some training members and bring about serious security risks. In order to solve differential attacks caused by semi-honest/malicious clients in federated training, this paper proposed a centralized differential privacy federated learning algorithm DP-FedAC. Firstly, the federal accelerated stochastic gradient descent algorithm was optimized to improve the aggregation mode of the server. After calculating the parameter update difference, the global model was updated by gradient aggregation mode to improve the stable convergence. Then, by adding centralized differential Gaussian noise to the aggregation parameters to hide the contributions of training members, the purpose of protecting the privacy information of participants was achieved. Time accounting (MA) was also introduced to calculate privacy loss to further balance the relationship between model convergence and privacy loss. Finally, comparative experiments were conducted with FedAC, distributed MB-SGD, distributed MB-AC-SGD and other algorithms to evaluate the comprehensive performance of DP-FedAC. The experimental results show that the linear acceleration of DP-FedAC algorithm is closest to that of FedAC in the case of infrequent communication, which is far better than the other two algorithms and has good robustness. In addition, the DP-FedAC algorithm achieves the same model accuracy as the FedAC algorithm on the premise of privacy protection, which reflects the superiority and usability of the algorithm.

Key words: federated learning, privacy leaks, differential privacy, Gaussian noise, privacy tracking

CLC Number:

TP309

XU Ruzhi, DAI Lipeng, XIA Diya, YANG Xin. Research on Centralized Differential Privacy Algorithm for Federated Learning[J]. Netinfo Security, 2024, 24(1): 69-79.

Figures/Tables 5

References 32

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保护策略	保护对象	保护方法	主要技术
中心	中心梯度^[3???-7] 中心模型^[8???-12]	扰动知识迁移	中心化差分隐私迁移、中心化差分隐私
本地	本地模型^[13,14]	扰动	中心化差分隐私
本地	本地属性信息^[15]	扰动	本地化差分隐私
本地	本地梯度^[16,17]	加密	同态加密秘密共享
中心/本地	本地梯度^[18??-21] 中心梯度^[22??-25]	安全混洗匿名	安全混洗（k, k^m）匿名本地化差分隐私
中心/本地	本地数据集信息和中心模型参数^[26]	安全多方计算与扰动	安全多方计算中心化差分隐私