Explanation

Federated learning is the correct approach for this healthcare scenario because:

• Privacy Preservation: Patient data remains on-premises at each hospital and is never shared or transmitted to a central server
• Distributed Training: Machine learning models are trained locally at each hospital using their own patient data
• Model Aggregation: Only model updates (weights/parameters) are sent to a central server, not the raw data
• Compliance: This approach respects privacy laws like HIPAA that restrict sharing of patient health information

Why other options are incorrect:

• Supervised learning (A): Requires labeled data to be centralized, which violates privacy constraints
• Self-supervised learning (C): Still typically requires data to be accessible in one location
• Transfer learning (D): Involves transferring knowledge from one domain to another, but doesn't address the distributed privacy-preserving training requirement

Federated learning enables collaborative model training across multiple institutions while maintaining data privacy and regulatory compliance.