ArXiv Preprint
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.
Junu Kim, Kyunghoon Hur, Seongjun Yang, Edward Choi
2022-11-14
