Learn Electronic Health Records by Fully Decentralized Federated Learning

Understand

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.