학술논문
Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments
Document Type
Conference
Source
2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2021 IEEE 26th International Workshop on. :1-6 Oct, 2021
Subject
Language
ISSN
2378-4873
Abstract
We consider a wireless federated learning system where multiple data holder edge devices collaborate to train a global model via sharing their parameter updates with an honest-but-curious parameter server. We demonstrate that the inherent hardware-induced distortion perturbing the model updates of the edge devices can be exploited as a privacy-preserving mechanism. In particular, we model the distortion as power-dependent additive Gaussian noise and present a power allocation strategy that provides privacy guarantees within the framework of differential privacy. We conduct numerical experiments to evaluate the performance of the proposed power allocation scheme under different levels of hardware impairments.