Federated learning with differential privacy via fast Fourier transform for tighter-efficient combining.

ABSTRACT: Spurred by the simultaneous need for data privacy protection and data sharing, federated learning (FL) has been proposed. However, it still poses a risk of privacy leakage in it. This paper, an improved Differential Privacy (DP) algorithm to protect the federated learning model. Additionally, the Fast Fourier Transform (FFT) is used in the computation of the privacy budget [Formula: see text], to minimize the impact of limited arithmetic resources and numerous users on the effectiveness of training model. Moreover, instead of direct analyses of the privacy budget ε through various methods, Privacy Loss Distribution (PLD) and privacy curves are adopted, while the number of artificial assignments hyperparameters is reduced, and the grid parameters delineated for FFT use are improved. The imp

SUBMITTER: Guo S 

PROVIDER: S-EPMC11538499 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature