Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare.

ABSTRACT: Robust data privacy regulations hinder the exchange of healthcare data among institutions, crucial for global insights and developing generalised clinical models. Federated learning (FL) is ideal for training global models using datasets from different institutions without compromising privacy. However, disparities in electronic healthcare records (EHRs) lead to inconsistencies in ML-ready data views, making FL challenging without extensive preprocessing and information loss. These differences arise from variations in services, care standards, and record-keeping practices. This paper addresses data view heterogeneity by introducing a knowledge abstraction and filtering-based FL framework that allows FL over heterogeneous data views without manual alignment or information loss. The knowledg

SUBMITTER: Thakur A 

PROVIDER: S-EPMC11484763 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature