Exploring the implementation of federated learning in healthcare: a comprehensive review

Abstract

The healthcare industry is facing mounting pressure due to population increase, a higher prevalence of diseases, and an expanding volume of patient data collected by hospitals. While machine learning (ML) has been successfully used to develop data management systems, the enormous volume of sensitive medical data and privacy issues frequently limit its broad use. Furthermore, the absence of accurate and organized clinical data continues to be a key impediment to the efficient application of standard ML models in healthcare. To solve these challenges, Federated Learning (FL), an advanced method within the larger ML field, has emerged as a possible option. FL provides collaborative model training without the requirement to centralize data, ensuring anonymity while managing large datasets from multiple institutions. This paper provides a comprehensive review of recent research on Federated Learning for Healthcare (FLHC) with deep learning models. It describes the development framework of FLHC systems for various healthcare activities, as well as cutting-edge methodologies based on deep-federated learning architectures. Also, it examines the common benchmark datasets and evaluation criteria used in FLHC, emphasizing numerous obstacles and intriguing research avenues in the field. This thorough study seeks to provide a wide view on FLHC by exploring the most recent approaches and tactics for improving healthcare data management while resolving privacy and accuracy concerns.