The Internet of Medical Things (IoMT) represents a convergence of sensors, healthcare devices, and the Internet of Things (IoT), aiming to enhance healthcare services intelligently. Despite its potential, security and privacy concerns have impeded widespread integration, leading to a scarcity of high-quality IoMT datasets. Federated learning (FL), an emerging distributed learning method, offers enhanced security and privacy, making it beneficial for IoMT networks and smart healthcare systems (SHS). In the context of safeguarding critical networks against evolving cyber threats, intrusion detection systems (IDS) and ransomware have become indispensable. However, existing security models pose challenges and are often computationally expensive, particularly for resource-limited medical IoT devices. In this paper, we propose a privacy-preserving FL-based IDS model, designed to identify cyberattacks within IoMT networks. This model enhances the data privacy and security in IoMT to identify the cyberattacks in wearable devices, implantable devices, medical equipment, and logistic systems. The study demonstrates that integrating FL with 6G capabilities results in a robust and expandable security framework for medical infrastructures, protecting patient data while preserving functionality. All things considered, this strategy provides an intelligent, safe, and well-prepared solution for the healthcare systems of the future.