Resilient Stream Workflow Orchestration with Multi-Layered Federated Edge Computing for Dynamic IoT Data Applications

Abstract

Background Information: The IoT produces large volumes of data that require innovative techniques for real-time processing and analytics. Traditional centralized systems suffer from scaling and latency problems. Federated learning, hybrid cloud-edge computing, and resilient workflow orchestration can offer privacy, efficiency, and adaptability and help address such challenges for modern IoT ecosystems. swith resilient workflows and multi-layer task allocation for IoT analytics. The prime objectives are to reduce latency, optimize cost efficiency, enhance model accuracy, and better utilize resources. This study will also show how these integrated methods can scale up and are reliable in dynamic IoT environments. Methods: The approach uses federated learning for privacy-preserving analytics, resilient workflows for fault tolerance, and hybrid task allocation for efficiency. The evaluations were done in terms of performance metrics such as latency, cost efficiency, model accuracy, and resource utilization. The individual methods versus their combinations were compared, showing the effectiveness of integrated approaches. Results: The framework reached the lowest latency at 18.4 ms, highest cost efficiency at 92.5%, best model accuracy at 94.8%, and optimal resource utilization at 95.1%. Combined methods outperformed individual approaches, thereby validating the robustness and scalability of the framework for real-time IoT analytics and decision-making in dynamic environments. Conclusion: Experiments show the effectiveness of federated learning, resilient workflows, and task allocation for scalable IoT analytics. Enhancements to adapt in various scopes with adaptive learning, blockchain integration, and domain-specific applications will be the future improvements to make the framework even more applicable and robust in performance, bringing about real-time processing across diverse IoT use cases.