Federated Learning is a machine learning technique that trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. This is particularly advantageous in the healthcare sector, where patient data privacy is paramount. By sharing model updates instead of raw data, federated learning ensures that sensitive information remains secure and compliant with regulations like HIPAA and GDPR.
