Federated Deep Learning System for Application of Healthcare in Pandemic Situation

Abstract

In the field of healthcare, federated learning (FL) has become a game-changing machine learning technique, especially when it comes to patient mortality prediction using electronic health record (EHR) data. This study emphasizes how FL helps patients, hospitals, and clinics to keep control over their data sources by protecting data privacy and ownership. FL guarantees the security of private medical data by decentralizing data sources, lowering the possibility of unwanted access. Because of its adaptability, FL can be used in a variety of healthcare environments, including as hospital care, home healthcare, and mobile healthcare services. Its incorporation with blockchain technology improves data security even more and encourages cross-device collaborative learning without depending on a central aggregator. The creation of AI-based healthcare solutions, including the Internet of Medical Things (IoMT) architectures, clinical trial optimization, and precision medicine, is made easier by this integration. FL implementation in healthcare has obstacles including non-Inactive Ingredient Database (IID) features, hyperparameter framework search, communication rounds, data distribution, computing limitations of edge devices, and remote client collaboration, despite its potential advantages. To fully realize FL’s promise in transforming healthcare delivery and enhancing patient outcomes, several challenges must be overcome.

Ähnliche Arbeiten