A Hybrid Federated Learning and Differential Privacy Model for Secure Personalized Health Data Analytics
Keywords:
Federated Learning, Differential Privacy, Health Data, Secure Analytics, Hybrid Model, Privacy-Preserving Machine LearningAbstract
With the exponential growth of health data from wearable devices and hospital systems, preserving patient privacy while enabling high-quality analytics has become paramount. Federated Learning (FL) has emerged as a decentralized solution, allowing model training across distributed datasets without centralizing sensitive data. However, FL alone does not fully address all privacy concerns. This paper proposes a hybrid model combining Federated Learning with Differential Privacy (DP) to enhance the privacy guarantees of health data analytics systems. Our approach demonstrates improved privacy-utility trade-offs and robustness across various healthcare scenarios. Through empirical analysis and prior studies, we show that our model maintains accuracy while adhering to strict privacy constraints.
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