Task-Oriented Network Reliability for Federated Learning-Enabled Industrial Internet of Things

With the rapid development of Industry 5.0, the industrial Internet of Things (IIoT) plays an increasingly important role as a key information infrastructure supporting data collection, transmission, and decision-making. Federated learning-enabled IIoT (FL-enabled IIoT) systems deploy artificial intelligence (AI) models at the network edge and utilize model aggregation mechanisms to facilitate efficient data processing and intelligent decision-making. However, anomalies occurring in local nodes can propagate through the aggregation process, leading to model contamination and performance degradation, thereby compromising overall system reliability. To address this issue, this article proposes a reliability model for FL-enabled IIoT systems. In this model, we systematically describe the entire process of model performance degradation caused by node failures and its impact on system task reliability. This includes the effects of multiple functional failures induced by node faults (i.e., data loss, communication interruption, and computational resource degradation) on model performance, as well as the failure propagation process caused by model contamination and data quality deterioration. Additionally, to evaluate the impact of model performance variations on practical production tasks, a task-oriented reliability metric is proposed. The simulation and experimental results demonstrate that the proposed modeling approach effectively characterizes the model performance degradation process and task reliability under node failure conditions.