Federated learning based futuristic biomedical big-data analysis and standardization.

ABSTRACT: Medical data processing and analytics exert significant influence in furnishing dependable decision support for prospective biomedical applications. Given the sensitive nature of medical data, specialized techniques and frameworks tailored for application-centric processing are imperative. This article presents a conceptualization for the analysis and uniformitarian of datasets through the implementation of Federated Learning (FL). The realm of medical big data stems from diverse origins, necessitating the delineation of data provenance and attribute paradigms to facilitate feature extraction and dependency assessment. The architecture governing the data collection framework is intricately linked to remote data transmission, thereby engendering efficient customization oversight. The operat

SUBMITTER: Fathima AS 

PROVIDER: S-EPMC10550167 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature