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InfoResearchPeer-reviewed
A review of recent advances in privacy-preserving federated learning for healthcare applications
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Summary
This review examines recent developments in privacy-preserving federated learning for healthcare, covering medical imaging, EHR analytics, genomics and wearable monitoring, with work published between 2024 and early 2026. It analyses challenges including non-IID data, communication overhead, gradient leakage, model poisoning and membership inference attacks, along with regulatory and interoperability concerns. The paper was published in Discover Applied Sciences on 2026-10-16.