Privacy-Preserving Machine Learning on Clinical Data Using Federated Learning and Differential Privacy in Compliance-Constrained Environments
Keywords:
Federated Learning, Differential Privacy, Clinical Data, Privacy-Preserving Machine Learning, Healthcare AI, Regulatory ComplianceAbstract
The increasing digitization of healthcare data has enabled advanced machine learning applications while simultaneously amplifying privacy, security, and regulatory concerns. Traditional centralized learning approaches conflict with compliance requirements such as HIPAA and GDPR due to risks associated with data sharing and re-identification. This research paper explores the integration of Federated Learning (FL) and Differential Privacy (DP) as a privacy-preserving machine learning framework for clinical data analysis.
This study reviews existing literature, system architectures, and compliance-driven constraints, highlighting the practical trade-offs between privacy, utility, and scalability. It demonstrates that combining federated learning (FL) and differential privacy (DP) can support regulatory compliance while preserving clinically meaningful model performance.
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