# Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments

arXiv: https://arxiv.org/abs/2102.10639 · HTML: https://mlsanity.com/paper/2102.10639/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2021.

## Built on by (most cited first, 2 of 2)

- [Differentially Private Federated Learning via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2203.17028/) (2022)
- [Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channels](https://mlsanity.com/paper/2306.10982/) (2023)

## Abstract (arXiv)

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.