Decentralized Federated Learning for Electronic Health Records
Songtao Lu, Yawen Zhang, Yunlong Wang
Abstract
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 (DFL) 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.