Federated Quantile Regression over Networks
Liqi Huang, Xin Wei, Peikang Zhu, Yun Gao, Mingkai Chen, Bin Kang · 2020
In order to solve the issue of isolated data islands and data security and personal privacy in the development of artificial intelligence, federated machine learning effectively solves the problem of sharing knowledge while protecting user privacy and data security. In a wireless sensor network, a secure learning framework is particularly needed, so that each sensor node can jointly learn knowledge without leaking local node data. Compared to traditional regression analysis algorithms, quantile regression can more fully describe the relationship between response values and its covariates by estimating conditional quantile sequences rather than a single value (such as the mean). In this paper, we propose a quantile regression federated learning framework that applies quantile regression with federated learning frameworks to wireless sensor networks, studies the performance of the algorithms, and the effectiveness of the algorithms is verified by simulations.