Federated Learning for Healthcare Informatics

Area of Science:

• Computer Science
• Biomedical Informatics
• Data Science

Background:

• Growing availability of healthcare data from diverse sources (clinics, patients, insurers).
• Data fragmentation and privacy concerns hinder the development of robust, generalizable analytical models.
• Need for "big data" approaches to derive meaningful insights and enhance healthcare quality.

Purpose of the Study:

• To survey federated learning (FL) technologies within the biomedical domain.
• To review solutions for statistical, system, and privacy challenges in FL.
• To highlight the implications and potential of FL in healthcare.

Main Methods:

• Review of federated learning principles and applications.
• Analysis of challenges in implementing FL for healthcare data.
• Discussion of privacy-preserving techniques in distributed machine learning.

Main Results:

• Federated learning offers a promising approach to integrate fragmented healthcare data.
• Identified solutions address statistical, system, and privacy hurdles in FL.
• Demonstrated the potential of FL for privacy-preserving big data analytics in medicine.

Conclusions:

• Federated learning is crucial for unlocking insights from distributed health data.
• Overcoming FL challenges is key to advancing data-driven healthcare.
• FL has significant potential to improve medical research and patient care through secure data collaboration.