  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.
