Secure Federated GenAI for Healthcare IoT Networks

Abstract: The combination of Healthcare Internet of Things (HIoT) and Generative Artificial Intelligence (GenAI) has offered an eminent opportunity to develop highly personalized, real-time healthcare offerings. However, the need for data security, cloud dependency, and sensitivity in handling medical information have come in the way of mass adoption. This paper proposes a secure Federated GenAI framework for HIoT networks to allow distributed training and generation of AI models directly on edge devices like wearables and mobile health sensors. By combining federated learning protocols with privacy-preserving mechanisms and generative AI, the system minimizes the need for transmitting raw data to centralized cloud servers. This architecture asserts data sovereignty, minimizes the risk of data breaches, and preserves the performance of models operating across distributed nodes. The experimental evaluation showed the framework achieves competitive accuracy levels for health monitoring tasks along with strong privacy guarantees and communication efficiency. Therefore, our results signal that secure Federated GenAI can be a credible base for developing scalable and ethical AI-driven healthcare systems, particularly in settings that are resource-constrained or laden with regulations.

Healthcare IoT (HIoT), Federated Learning, Generative AI, Edge Computing, Data Privacy, Medical AI, Wearable Sensors, Privacy-Preserving Machine Learning, Secure Distributed Systems, Decentralized Health Monitoring

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