aceA review of recent advances in privacy-preserving federated learning for healthcare applications
The rapid adoption of artificial intelligence (AI) in healthcare, driven by the increasing availability of electronic health records (EHRs), medical imaging systems, genomic data, and wearable devices, has created significant challenges related to patient privacy, data security, and regulatory compliance. Traditional centralised machine learning (ML) approaches often require large-scale data sharing, which increases the risk of sensitive medical information exposure. Federated learning (FL) has emerged as a prom…
Federated learning allows multiple healthcare institutions to train models together without transferring raw patient data, and added privacy techniques strengthen its use in imaging, health records, genomics and wearables.
Federated healthcare systems still face gradient leakage, model poisoning, membership inference attacks, non-IID data, communication overhead and scalability problems.
The source describes remaining technical, security, regulatory and interoperability challenges, and identifies research gaps, so federated learning's privacy benefits are not shown to be fully resolved.
Evidence
- Peer-reviewedDiscover Applied Sciences2026-10-16
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Truvace Impact Record TRV-2026-1689, v1: “A review of recent advances in privacy-preserving federated learning for healthcare applications.” Truvace, 2026-10-17. /record/TRV-2026-1689 (accessed at citation time). sha256 9f5e3dbc4090b85f…
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