Federated Learning for Secure and Privacy‐Aware Internet of Medical Things: Taxonomy, Emerging Applications, Open Challenges, and Future Directions

ABSTRACT The healthcare industry, particularly with the advent of the Internet of Medical Things (IoMT), has witnessed significant integration of Internet of Things (IoT) technologies. IoMT is transforming healthcare by providing substantial benefits to both consumers and healthcare providers. However, the exponential growth in IoMT devices and their data generation raises critical challenges related to data analysis, security, and privacy. Traditional centralized artificial intelligence (AI) approaches, reliant on deep learning (DL) and machine learning (ML) algorithms, struggle to address the increasing complexity of sensitive medical data due to scalability and privacy concerns. Federated Learning (FL) emerges as a promising solution, enabling collaborative model training directly on IoMT devices while preserving data privacy by transmitting only model updates to central servers. This approach ensures data confidentiality and addresses privacy concerns associated with centralized systems. Despite its potential, research on FL in the context of IoMT remains limited. This paper examines the latest developments and innovations in FL, focusing on its application in IoMT and smart healthcare systems. It explores FL architectures, aggregation algorithms, frameworks, and their integration into IoMT‐driven healthcare applications. Additionally, the paper highlights challenges, including data heterogeneity, communication overhead, and security vulnerabilities, alongside privacy‐preserving techniques such as differential privacy, homomorphic encryption (HE), and secure multiparty computation (SMC). Finally, it identifies future research directions to advance FL‐powered IoMT solutions, offering valuable insights for academia and industry stakeholders aiming to enhance privacy‐preserving, intelligent healthcare systems.