The Internet of Medical Things (IoMT) has largely revolutionized the healthcare sector because of its rapid growth that allows continuous monitoring and data-driven services that are based on intelligence. Nonetheless, such a rising connectivity also heightens the susceptibility of sensitive medical data such that, strong security and privacy-driving solutions are required. In order to overcome these issues, this paper presents SMOFEL (Spider Monkey Optimized Federated Extreme Learning) which is an integrated system that incorporates Federated Learning (FL), Extreme Learning Machine (ELM), Spider Monkey Optimization (SMO), and AI-Based Blockchain Technology. FL enables decentralized training of models by making sure that raw patient data are stored on local IoMT devices, which improve privacy and regulation. SMO enhances the convergence and optimization of parameters in the learning process, which is why the framework can be used in resource-constrained IoMT settings. Data integrity is also enhanced with the help of blockchain technology as it offers an immutable and transparent list of model updates and safe transactions. Smart contracts provide the capability to enter into automated and immutable data-sharing contracts across involved nodes. Simulated healthcare data experimental assessment proves that SMOFEL supports an accuracy of 98.08, which indicates its potential to increase the security, efficiency, and predictive power. Altogether, the suggested framework presents a holistic way to achieve secure, scalable, and privacy-saving healthcare analytics, and SMOFEL can be a great solution to next-generation IoMT ecosystems.
Nishok et al. (2026) studied this question.