Intelligent IoT-enabled healthcare solutions implementing federated meta-learning with blockchain

Abstract

The rapid advancement and incorporation of Artificial Intelligence (AI) and the Internet of Things (IoT) have created exceptional opportunities to revolutionize healthcare and treatment methods and offer significant potential for broader industrial information integration. Nevertheless, the growth of intelligent healthcare systems faces challenges such as data confidentiality concerns and the safety of AI algorithms. The need for local datasets is the main problem in applying traditional AI to the development of a personalized model for health care. Thus, to tackle these issues, a novel healthcare system based on blockchain powered by federated matrix meta-learning supported by IoT. In this system, IoT devices function as light nodes, uploading local, shareable information to an edge server for model training, while non-tampered models downloaded through smart contracts handle local private data. This framework comprises four key modules: a hierarchical feature extraction module, a graph topology formulation unit, a dynamic prototype optimization algorithm, and a predictive query integration system. Blockchain technology ensures the healthcare model remains consistent and protects private data from leaks. Also, it has offered a federated matrix meta-learning model known as the federated Matrix-prototype Graph Network (MGN) to handle heterogeneous healthcare data efficiently. This model, based on metrics and graph networks, excels at capturing data distributions even with limited labeled data. To validate the efficacy of the proposed framework, we conducted extensive evaluations using two widely recognized datasets: CheXpert only for medical imaging and CIFAR 100 for general image classification. These experiments increased the performance of up to 85 percent of existing healthcare systems, demonstrating the potential of the proposed integrated approach to solve the industry’s main problems. Thus, this study advances the current discourses on the development of strong, privacy-oriented, and context-aware AI solutions for health systems, which, together with intelligent health technology, will help to raise the efficiency and effectiveness of patient care in the future.