Federated Learning in Healthcare: Balancing Data Privacy and Predictive Accuracy in Multi-Institutional Settings

Abstract

Federated Learning (FL) offers a compelling paradigm for training machine learning models across multiple healthcare institutions without centralizing sensitive patient data. By enabling local training and sharing only model updates, FL promises to reconcile clinicians’ need for large, diverse training populations with legal and ethical constraints on data sharing. However, practical deployments must carefully balance privacy (minimizing leakage of patient information) and predictive accuracy (learning performant models under non-i.i.d. data, heterogeneity, and limited labels). This article presents a comprehensive, scholarly framework for federated learning in healthcare. We synthesize theoretical foundations (optimization and privacy guarantees), examine system-level techniques (secure aggregation, differential privacy, cryptographic protections), survey domain-specific considerations (EHRs, medical imaging, genomics), and analyze trade-offs that institutions must make when adopting FL. We also provide practical guidance for implementation, evaluation metrics, and prospective directions covering personalization, fairness, adversarial robustness, and nascent quantum-assisted approaches. Throughout, we emphasize operational constraints in regulated environments (HIPAA, GDPR) and propose guidelines for achieving an acceptable balance between data privacy and predictive accuracy in multi-institutional clinical collaborations.