Privacy-Preserving Federated Learning for Distributed Healthcare Analytics Platforms

Abstract

Patient data is uniquely sensitive and, under HIPAA, the GDPR, and comparable regimes, is rarely permitted to leave the
institution that collected it, yet the machine learning models most needed in medicine are exactly the ones that benefit from
scale and demographic diversity no single hospital can supply alone. Federated learning offers a way to train shared models
across many institutions' data without centralizing it, but sharing model updates instead of raw records is not, by itself, a
sufficient privacy guarantee: gradients can leak training data, and trained models can leak membership. This article
synthesizes the literature on privacy-preserving federated learning for distributed healthcare analytics, tracing the field from
federated averaging and its cross-silo healthcare variant through the differential privacy, secure aggregation, and
encryption-based mechanisms developed to close the gaps plain federation leaves open, the multi-institutional deployments on
real clinical data that have validated the pattern in oncology, infectious disease, and general clinical prediction, and the attack
literature that motivates the privacy layer in the first place. Read together, the evidence supports privacy-preserving federated
learning as a genuinely validated foundation for distributed healthcare analytics platforms, while leaving standardized
cross-study evaluation, quantified privacy-utility reporting, and robustness to heterogeneous or adversarial participants as an
open research agenda