This paper presents a differentially private algorithm for linear regression learning in a decentralized fashion. Under this algorithm, privacy budget is theoretically derived, in addition to that the solution error is shown to be bounded by O(t) for O(1/t) descent step size and O(exp(t1−e)) for O(t−e) descent step size.
@article{arxiv.2004.07425,
title = {Differentially Private Linear Regression over Fully Decentralized Datasets},
author = {Yang Liu and Xiong Zhang and Shuqi Qin and Xiaoping Lei},
journal= {arXiv preprint arXiv:2004.07425},
year = {2020}
}