Universal EHR Federated Learning Framework
This addresses privacy-preserving multi-source learning for electronic healthcare records, but it is incremental as it combines existing methods.
The paper tackled the challenges of heterogeneous EHR systems and non-i.i.d. data in federated learning by combining a unification framework with FL, resulting in an average 3.4% performance gain over local learning.
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.