Federated Learning in Healthcare: From Research to Real-World Deployment.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Machine Learning

Background:

• Artificial intelligence (AI) shows promise for healthcare but faces challenges in clinical translation due to data access limitations.
• Centralized learning models for multi-institutional collaboration are hindered by privacy, legal, and logistical issues.
• Federated learning (FL) offers a decentralized solution for collaborative AI development without raw data sharing.

Purpose of the Study:

• To review algorithmic, privacy, and practical advancements in federated learning for biomedical applications.
• To discuss strategies for handling data heterogeneity and ensuring privacy in FL.
• To identify limitations and infrastructure needs for scalable FL deployment in healthcare.

Main Methods:

• Review of key developments in federated learning algorithms for biomedical engineering.
• Analysis of privacy-preserving techniques including differential privacy, secure aggregation, and confidential computing.
• Discussion of practical considerations for infrastructure and interoperability.

Main Results:

• Federated learning facilitates collaborative model training while preserving patient data privacy.
• Strategies exist to address non-identical data distributions across institutions.
• Scalable and interoperable infrastructures are crucial for widespread FL adoption.

Conclusions:

• Federated learning represents a paradigm shift for developing generalizable, equitable, and clinically impactful AI models.
• Continued advancements in FL-as-a-service platforms and regulatory-aligned workflows are necessary.
• Trustworthy and persistent deployment of FL models is key to realizing AI's potential in patient care.