Abstract

The proliferation of Internet of Things (IoT) devices in healthcare has generated vast amounts of sensitive patient data, creating opportunities for personalized and proactive care. However, directly centralizing this data poses significant privacy risks. This paper proposes a novel framework that integrates Federated Learning (FL) and Differential Privacy (DP) to address these challenges. FL enables collaborative model training across decentralized IoT devices without sharing raw data, while DP provides rigorous privacy guarantees by adding controlled noise during the learning process. Our approach enhances security and personalization in IoT healthcare applications by enabling robust model development while preserving patient confidentiality. We present a detailed methodology, experimental results on simulated healthcare datasets, and a thorough discussion of the trade-offs between privacy, accuracy, and communication efficiency. The results demonstrate the feasibility and effectiveness of the proposed framework for improving healthcare outcomes while maintaining stringent data privacy standards. We also explore the challenges of implementing this framework in real-world scenarios and suggest potential future research directions, including adaptive privacy mechanisms and optimized communication protocols.