AWS Certified Cloud Practitioner
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A healthcare network operates hospitals in multiple regions. Due to privacy laws, patient data cannot be shared directly, but models must learn from patterns across all hospitals. Which ML approach best supports this?
Real Exam
Community
RRitesh
0
A
Supervised learning
B
Federated learning
C
Self-supervised learning
D
Transfer learning
Explanation:
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.
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