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arXiv · 2211.07300

Universal EHR Federated Learning Framework

Junu Kim·Kyunghoon Hur·Seongjun Yang·Edward Choi
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Abstract

Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR). Despite its guarantee of privacy protection, the wide application of FL is restricted by two large challenges: the heterogeneous EHR systems, and the non-i.i.d. data characteristic. A recent research proposed a framework that unifies heterogeneous EHRs, named UniHPF. We attempt to address both the challenges simultaneously by combining UniHPF and FL. Our study is the first approach to unify heterogeneous EHRs into a single FL framework. This combination provides an average of 3.4% performance gain compared to local learning. We believe that our framework is practically applicable in the real-world FL.

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Junu Kim, Kyunghoon Hur, Seongjun Yang, Edward Choi. 2022-11-14. Universal EHR Federated Learning Framework. https://arxiv.org/abs/2211.07300

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