Federated Learning in Smart Healthcare: Challenges and Secure Frameworks

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) applications in medical domain encounter significant barriers due to privacy regulations and data fragmentation across healthcare institutions. Decentralized machine learning frameworks enable collaborative model development while maintaining data confidentiality at source locations. This comprehensive analysis examines distributed learning methodologies applied to two critical healthcare domains: cardio-vascular risk assessment and confidential information exchange systems. We systematically classify current approaches according to training methodologies, cryptographic protections, and adaptation techniques. The investigation evaluates model effectiveness across diverse clinical environments with heterogeneous data distributions and operational protocols. Secure communication mechanisms including encrypted computation and statistical privacy safeguards are analyzed for their defensive capabilities against adversarial exploitation. The survey concludes by identifying research opportunities in model customization, resource efficient implementations, and ethical governance frameworks for real world clinical adoption.

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