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arXiv · 1912.01792

Learn Electronic Health Records by Fully Decentralized Federated Learning

Songtao Lu·Yawen Zhang·Yunlong Wang·Christina Mack
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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 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.

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Songtao Lu, Yawen Zhang, Yunlong Wang, Christina Mack. 2019-12-04. Learn Electronic Health Records by Fully Decentralized Federated Learning. https://arxiv.org/abs/1912.01792

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