Definition of a novel federated learning approach to reduce communication costs

摘要

Background and Objective:Contemporary Machine Learning approaches (e.g., Deep Learning) need huge volumes of data to build accurate and robust statistical models. Nowadays, very often, such data are collected by distinct and geographically distributed entities and successively transmitted to and stored by centralized nodes that implement the learning process. This practice, however, exposes data to security and privacy risks that may be even unacceptable in those environments regulated by the General Data Protection Regulation (GDPR).