AWS Certified Cloud Practitioner

Explanation

Federated learning is the correct approach for this scenario because:

1. Privacy Preservation: Federated learning allows machine learning models to be trained across multiple decentralized devices or servers holding local data samples, without exchanging the data itself. This aligns perfectly with privacy laws that prevent direct sharing of patient data.

2. Decentralized Training: In federated learning, the model is sent to each hospital's local server, trained on local patient data, and only the model updates (not the raw data) are sent back to a central server for aggregation.

3. Cross-Institutional Learning: This approach enables learning from patterns across all hospitals while keeping sensitive patient data within each hospital's secure environment.

Why other options are incorrect:

• A) Supervised learning: Requires labeled data to be centralized, which would violate privacy laws by sharing patient data directly.

• C) Self-supervised learning: Typically works on unlabeled data but still requires data to be centralized for training.

• D) Transfer learning: Involves using pre-trained models and fine-tuning them on new data, but still requires access to the target data for fine-tuning.

Federated learning is specifically designed for scenarios where data privacy and decentralization are critical requirements, making it ideal for healthcare applications with strict privacy regulations.