IoT architectures are commonly structured [7] into multiple layers to manage various functionalities, including data acquisition, communication, processing, and security [8]. The most basic models define a three-layer architecture comprising the Perception Layer, which collects data from physical devices; the Network Layer, which transmits data; and the Application Layer, which processes and presents information to users. For instance, some models expand into a five-layer architecture by incorporating the Middleware Layer, responsible for data management and interoperability; and the Business Layer, which provides high-level decision making and analytics. More comprehensive frameworks are extended to seven, eight, or even nine layers by adding elements such as the Edge Computing Layer, which improves real-time data processing, the Security Layer; ensuring data protection [9] and privacy [10]; and the Management Layer, which oversees system operations and maintenance [11]. Given the diversity of IoT architectures, our objective is to design a flexible and adaptable framework that ensures seamless integration, regardless of the number of layers used. Scalability is a critical concern in IoT implementations [12], with new sensors [13], new devices, and new metrics added in real time becoming a norm [14]. Architectures have to seamlessly adapt to such additions without impacting current operations. Public sector agencies with the intention of introducing state-of-the-art service delivery solutions will demand this adaptability [15]. Ontologies [16], through their structured form of representing and storing information, play a key role in managing IoT system complexity [17]. Including new capabilities while being compatible with older ones [18], however, is a most challenging activity. Such adaptability will be necessitated in managing the continuous expansion and proliferation of IoT use cases [19]. Balancing data locality with communication efficiency is a critical concern. Since edge servers operate in standalone environments, the locality of data must be preserved in a manner as to not reveal sensitive information over untrusted networks [20]. Distributed training approaches address such an issue through model training in a cooperative manner with reduced data sharing [21]. Sustaining such a balance is not only critical for system performance but also for trust development in such privacy-sensitive scenarios, in which security of information holds a topmost position [22]. Real-time adaptability is critical for long-term operability in dynamically changing IoT environments. With new devices, metrics, and applications added periodically, the system must adapt and integrate seamlessly with such additions and updates [23]. There must be robust ontology evolution and reconciliation of data allowing for uniformity and accuracy in heterogeneous nodes [24]. Inadequate, slow or delayed adaptations can introduce inefficiencies, most noticeably in mission-critical environments, where real-time responsiveness cannot be compromised [25]. To tackle such a challenge, a multi-faceted answer combining semantic data standardization with scalable,decentralized learning is a necessity. Not only does it enable interoperability between a range of IoT devices, but it can also effectively train machine learning algorithms in low-resource environments. It must, in addition, have enough adaptability to respond to continuous IoT device and metric creation, ensuring its effectiveness and pertinency over a duration of time. The interplay between interoperability and distributed learning is key in creating future IoT systems. In this contribution, a new model fusing ontology-based data standardization with state-of-the-art federated learning approaches is proposed. By fusing both, the proposed scheme addresses the challenge in decentralized IoT settings, paving the way for IoT applications with enhanced adaptability and efficiency.