IoT-edge computing, ontology-driven standardization, Federated proximal learning (FedProx), data interoperability, scalability in decentralized systems, non-IID data processing, Information technology, T58.5-58.64

The rapid expansion of IoT devices has introduced major challenges in ensuring data interoperability, enabling real-time processing, and achieving scalability, especially in decentralized edge computing environments. In this paper, an advanced framework of FedProx with ontology-driven data standardization is proposed, which can meet such challenges comprehensively. On the one hand, it can guarantee semantic consistency across different kinds of IoT devices using unified ontology, so that data from multiple sources could be seamlessly integrated; on the other hand, it solves the non-IID issues of data and limited resources in edge servers by FedProx. Experimental findings indicate that FedProx outperforms FedAvg, with a remarkable accuracy level of 89.4%, having higher convergence rates, and attaining a 30% saving on communication overhead through gradient compression. In addition, the ontology alignment procedure yielded a 95% success rate, thereby ensuring uniform data preprocessing across domains, including traffic monitoring and parking management. The model demonstrates outstanding scalability and flexibility to new devices, while maintaining high performance during ontology evolution. These findings highlight its great potential for deployment in smart cities, environmental monitoring, and other IoT-based ecosystems, thereby enabling the creation of more efficient and integrated solutions in these areas.