I. Introduction
Federated learning (FL) was first proposed by Google as a new Machine Learning (ML) framework [1]–[3]. With the framework of FL, android mobile phone terminals can train their local models using their own data on the phones, and then send the trained local models to a cloud server that aggregates all local models into a global model. In the learning process of FL, the cloud server does not know each terminal's data, but its aggregated model can benefit from all data at the terminals [4]. This FL approach solves the problem of data leakage in traditional ML by allowing data owners to store data in their own hands but training a globe model using all data [2].