The capability of mobile devices to sense and compute at an advanced level has significantly improved in recent years, and combined with the advancements in Deep Learning (DL) has led to a vast range of opportunities for different practical applications such as healthcare and vehicular networks. However, traditional cloud-based Machine Learning (ML) approaches require the data to be stored in a cloud server, resulting in critical issues such as poor communication efficiency, high latency and privacy concerns. The idea to bring intelligence closer to the edge was proposed. However, the conventional technologies that enable Machine Learning (ML) at edge networks require sharing of personal data, which defies strict data privacy regulations.