文献类型：期刊文献

英文题名：Federated Learning with Differential Privacy Via Fast Fourier Transform for Tighter-Efficient Combining

作者：Guo, Shengnan Yang, Jianfeng Long, Shigong Wang, Xibin Liu, Guangyuan

第一作者：Guo, Shengnan

机构：[1] State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China; [2] School of Big Data, Laboratory of Electrical Power Big Data of Guizhou Province, Guizhou Institute of Technology, Guiyang, 55005, China

第一机构：State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

年份：2023

外文期刊名：SSRN

收录：EI(收录号：20230364241)

语种：英文

外文关键词：Budget control - Learning algorithms - Learning systems - Privacy-preserving techniques

摘要：Spurred by the simultaneous need for data privacy protection and data sharing, federated learning has been proposed. However, there is still a risk of privacy leakage in it. In this paper, an improved differential privacy algorithm is proposed to protect the federated learning model. And at the same time, the Fast Fourier Transform is used in the computation of the privacy budget , to minimize the impact of limited arithmetic resources and numerous users on the effectiveness of the training model. Then, discarding the various discussions that were directly on the privacy budget instead, FFT is used together with PLD in this process for calculating consumption, which further tightens the bound of computation with minimal impact on the efficiency. Moreover, the activation function for model training is improved by using a temper sigmoid with only one parameter , which much smoother the accuracy curve and reduces the drastic fluctuating scenarios. Finally, simulation results on real datasets show that the federated learning with the DP algorithm that considers the long trailing case facilitates better equalizing the relationship between privacy and utility. ? 2023, The Authors. All rights reserved.