I. Introduction
Federated learning is a distributed learning paradigm in machine learning, where clients train a shared model in a collaborative way while preserving their local data [1]. The original model for federated learning includes a central server that does the coordination. The central server serves as the master, while the clients serve as the workers. The clients only send their model updates to the server at each iteration. Hence, the central server has no access to the clients' local data. The central server aggregates the updates received from all the clients at each iteration, and broadcasts a global model back to all the clients. This continues till the optimal global model is reached. Federated learning has been successfully applied for next word prediction in natural language processing [2], emoji prediction [3], vocabulary estimation [4], vehicle-to-vehicle and wireless communication [5]–[7], social networks [8]–[10] and predictions in health [11], [12].