Recently, federated learning (FL) has received intensive research because of its ability in preserving data privacy for scattered clients to collaboratively train machine learning models.

Furthermore, the current architecture of a server connected to multiple clients is highly sensitive to communication failures and computational overload at the server. In this work, we develop a ...