  Federated learning opens a number of research opportunities due to its high
communication efficiency in distributed training problems within a star
network. In this paper, we focus on improving the communication efficiency for
fully decentralized federated learning over a graph, where the algorithm
performs local updates for several iterations and then enables communications
among the nodes. In such a way, the communication rounds of exchanging the
common interest of parameters can be saved significantly without loss of
optimality of the solutions. Multiple numerical simulations based on large,
real-world electronic health record databases showcase the superiority of the
decentralized federated learning compared with classic methods.

Machine Learning;Neural and Evolutionary Computing;Machine Learning