Guest Editorial of the Special Section on Federated Learning for Consumer-centric Internet of Medical Things (IoMT)

In Recent Years, The Intersection of Consumer Health and The Internet of Medical Things (Iomt), encompassing wearables, health-monitoring sensors, and smart health applications, has generated vast streams of consumer data and provided significant momentum for innovation in healthcare technology. These devices enable continuous patient monitoring, seamless communication among providers, and remote health management, creating new opportunities for healthcare providers and researchers. At the same time, integrating diverse consumer-based IoMT devices into unified, efficient health ecosystems presents persistent challenges around privacy, security, and interoperability. Traditional centralized machine-learning pipelines that collect and store data in one place heighten privacy and security risks, and the sheer volume of IoMT data can overload centralized systems, resulting in inefficiencies and bottlenecks.