AWS Certified AI Practitioner

Explanation

Federated learning is the correct approach for this scenario because:

1. Privacy Preservation: Federated learning allows training machine learning models 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. Distributed Training: In federated learning, the model is trained locally on each hospital's data, and only the model updates (gradients or parameters) are shared with a central server, not the raw patient data.

3. Cross-Institutional Learning: The central server aggregates these updates to improve the global model, enabling learning from patterns across all hospitals while keeping sensitive data localized.

Why other options are incorrect:

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