HiFEL-OCKT: Hierarchical federated edge learning with objective congruence and multi-level knowledge transfer for IoT ecosystems

The explosive growth of Internet of Things (IoT) data and the demand for real-time decisions necessitate edge intelligence to overcome the latency and bandwidth limitations of cloud-only processing. Real-world IoT ecosystems are characterized by their high heterogeneity, which results from a wide variety of devices, sensors, environments, data, tasks, and resources, posing significant communication and computation efficiency challenges, scalability issues, and privacy concerns for edge intelligence. We propose HiFEL-OCKT, a novel hierarchical federated edge learning methodology for addressing the realistic high heterogeneity of IoT ecosystems, while enabling efficient edge intelligence. The key novelty of our proposed HiFEL-OCKT methodology is the efficient and scalable deployment of temporal intelligence at the edge by exploiting the valuable knowledge flowing at this level, which we define with the learning objective evolution, to ensure robust edge personalization through objective congruent collaboration and multi-level knowledge transfer between IoT devices. Through extensive experiments on multiple IoT domains, including smart buildings and industrial IoT with heterogeneous real-world datasets, our HiFEL-OCKT approach uncovered the novel ability in collaborating various highly heterogeneous IoT devices from different ecosystem settings. Our approach demonstrates superior performance and efficiency compared to the state-of-the-art approaches, with an improvement rate as high as 87.57 % in the edge knowledge personalization, while achieving significant speedups as high as 4.38 × in local training.