Federated learning for privacy-preserving health data analytics
Abstract
In the field of artificial intelligence, federated learning (FL) has emerged as a game-changing approach that enables training models across multiple distributed systems data sources without requiring the transfer or centralization of raw data. Federated learning offers a convincing way to create strong prediction models while protecting individual privacy in the healthcare sector, where handling patient data requires extreme care due to strict regulations such as HIPAA and GDPR. This paper reviews the fundamentals of federated learning, looks at how it can be applied to health data analytics, including disease prediction, medical imaging, and electronic health records, and critically evaluates current issues like adversarial threats like model poisoning, statistical heterogeneity among data silos, and communication overhead. In addition, the study suggests an improved FL framework that strengthens the privacy-utility trade-off by integrating secure aggregation methods and differential privacy. The suggested method preserves competitive model accuracy while greatly lowering privacy leakage, according to experimental research based on benchmark healthcare datasets. Future research directions, such as tailored FL, cross-silo federation, and integration with edge computing for real-time clinical decision assistance, are discussed at the study's conclusion.
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