Learn Electronic Health Records by Fully Decentralized Federated Learning
This addresses communication bottlenecks in distributed training for healthcare data, but it is incremental as it builds on existing federated learning methods.
The paper tackled communication efficiency in fully decentralized federated learning over a graph by performing local updates and reducing communication rounds, achieving significant savings without loss of optimality as shown in simulations on real-world electronic health record databases.
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.