Secure Federated Learning for IoT-Driven Smart Healthcare Robots: A Blockchain and AI-DL Approach

Abstract

The rapid adoption of Internet of Things (IoT)-enabled smart healthcare robots has transformed patient monitoring, diagnosis, rehabilitation, and remote clinical assistance through intelligent and autonomous healthcare services. However, the continuous generation and exchange of sensitive medical information across distributed robotic platforms introduce significant concerns related to privacy, security, data integrity, and regulatory compliance. Conventional centralized deep learning frameworks expose healthcare systems to single-point failures, unauthorized data access, and communication bottlenecks, thereby limiting their applicability in critical medical environments. This study proposes a secure and intelligent framework that integrates Federated Learning (FL), Blockchain technology, and Artificial Intelligence-based Deep Learning (AI-DL) to establish a decentralized and privacy-preserving learning environment for IoT-driven smart healthcare robots. Federated learning enables collaborative model training without exposing raw patient data, while blockchain provides immutable transaction records, decentralized trust management, and secure model validation. Deep learning algorithms enhance disease prediction, robotic perception, and adaptive decision-making using locally trained models. The proposed architecture improves security, computational efficiency, scalability, interoperability, and model robustness while minimizing communication overhead and preserving patient confidentiality. The framework offers a sustainable and reliable solution for next-generation intelligent healthcare ecosystems supporting secure autonomous robotic healthcare services.