Answer: Federated learning

## 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.

Author: Ritesh Yadav