Differential Privacy with Federated Learning for Personalized Healthcare
Abstract
This paper explores the integration of differential privacy (DP) with federated learning (FL) to facilitate personalized healthcare applications while rigorously safeguarding patient privacy. Traditional machine learning approaches relying on centralized data collection pose significant risks to individual confidentiality. Federated learning offers a decentralized alternative, training models across distributed devices without direct data sharing. However, inherent vulnerabilities remain due to the model updates themselves potentially revealing sensitive information. This work addresses this challenge by introducing a framework that incorporates differential privacy mechanisms directly into the federated learning process. We detail a proposed algorithm that adds calibrated noise to model updates, ensuring that the influence of any single patient's data on the global model is limited. The core contribution is a novel approach to balancing the privacy guarantees of DP with the utility requirements of FL, particularly within the sensitive domain of healthcare. The results demonstrate the feasibility of achieving privacy-preserving personalized healthcare models and highlight potential avenues for future research. Mathematical notation and formulas are presented in plain text for seamless copy-pasting.
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