Federated Learning Application for Secure and Privacy-Aware Internet of Medical Things

Abstract

The healthcare industry has made extensive use of Internet of Things (IoT) technologies, particularly with the launch of the Internet of Medical Things (IoMT). IoMT is transforming healthcare by providing both patients and medical professionals with substantial benefits. However, the exponential growth of IoMT devices and the data they produce creates serious problems with data analysis, security, and privacy. Traditional centralized artificial intelligence (AI) techniques that depend on deep learning (DL) and machine learning (ML) algorithms find it challenging to manage the increasing complexity of sensitive medical data due to scalability and privacy concerns. Federated Learning (FL) is a promising approach that enables collaborative model training directly on IoMT devices while safeguarding data privacy by transmitting only model updates to central servers. This approach ensures data security and resolves privacy concerns associated with centralized systems. Despite its potential, there is currently little research on FL in the context of IoMT. This article examines the latest developments in FL with a focus on its use in IoMT and smart healthcare systems. It investigates the integration of FL architectures, frameworks, and aggregation approaches in IoMT-powered medical applications. Along with privacy-preserving techniques like differential privacy, homomorphic encryption, and safe multi-party computing, it also looks at problems like data diversity, communication costs, and security vulnerabilities. Finally, by describing future research objectives for the development of FL-based IoMT systems, it offers crucial information to commercial and academic parties interested in enhancing intelligent and privacy-preserving healthcare services.