A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics

Area of Science:

• * Health Informatics
• * Machine Learning
• * Data Privacy

Background:

• * Healthcare generates vast amounts of data, posing management challenges.
• * Traditional machine learning (ML) struggles with sensitive medical data due to privacy concerns.
• * Lack of precise clinical data hinders effective ML application in healthcare.

Purpose of the Study:

• * To survey the applications of Federated Learning (FL) in healthcare informatics.
• * To discuss the necessity and motivations for FL in the healthcare domain.
• * To highlight recent advancements and future research directions in FL for healthcare.

Main Methods:

• * Review of existing literature on Federated Learning (FL) in healthcare.
• * Analysis of FL fundamentals and motivations for healthcare applications.
• * Exploration of state-of-the-art FL applications across various healthcare verticals.

Main Results:

• * Federated Learning (FL) offers a promising solution to overcome privacy barriers in healthcare data analysis.
• * FL enables robust and dependable machine learning model development using distributed medical data.
• * Identified key applications, challenges, and future research avenues for FL in health informatics.

Conclusions:

• * Federated Learning (FL) is a critical advancement for leveraging machine learning in healthcare while preserving patient privacy.
• * The survey provides a comprehensive overview of FL's current impact and potential in health informatics.
• * Future research should focus on addressing open issues and challenges to fully realize FL's benefits in healthcare.