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A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics.

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Federated learning (FL) addresses healthcare data privacy issues, enabling machine learning (ML) to better utilize sensitive medical information for improved health outcomes. This survey explores FL

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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.