aceFederated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration
Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks…
Federated learning allows hospitals and health systems to train shared models without centralizing patient data, supporting real-time IoT and wearable monitoring for predictive analytics and personalized care.
Federated learning deployments in smart healthcare remain vulnerable to adversarial attacks, data poisoning, and model inversion, plus practical barriers of heterogeneous data, scalability, and system interoperability.
Effectiveness limited by unresolved security and systems challenges including adversarial attacks, data poisoning, model inversion, data heterogeneity, scalability, and interoperability.
Evidence
- Peer-reviewedHealthcare2024-12-22
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Truvace Impact Record TRV-2026-0550, v1: “Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration.” Truvace, 2026-07-24. /record/TRV-2026-0550 (accessed at citation time). sha256 43249da119b2a856…
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