The Internet of Multimedia Things (IoMT) is gaining speed due to the increase in multimedia data and the interconnectedness of real-world physical devices with the advent of the Internet of Things (IoT) and real-time multimedia data transmission services. The paper focuses on the IoMT's unique challenges and potential, particularly in the domains of security, privacy, and network efficiency. Our research pivots around two core areas: addressing network vulnerabilities in IoMT and enhancing data privacy, specifically in healthcare contexts. This includes an exploration of federated learning combined with homomorphic encryption, underlining their importance in maintaining data security and integrity. We investigate the balance between model performance and execution time, with a detailed analysis of classification metrics in both encrypted and plain data scenarios. This approach highlights the effectiveness of federated learning as a privacy-preserving method in healthcare IoMT applications. The synthesis of these findings illuminates the intricate balance required between security, privacy, and performance in IoMT. It underscores the potential of advanced ML models and privacy-preserving techniques in building a secure, resilient, and ethical digital ecosystem. Our study not only contributes to the advancements in network security and data privacy but also establishes a foundational framework for the responsible and innovative evolution of IoMT. This research fills a critical gap by focusing on the specific requirements and challenges of multimedia content in IoMT, advancing beyond the traditional IoT paradigms that predominantly consider scalar data. The IoMT model introduced here serves as a novel paradigm, envisioning smart, heterogeneous multimedia things interacting and cooperating to facilitate globally available multimedia-based services and applications.