Abstract
Intelligent manufacturing systems progressively advance toward highly collaborative and distributed decision making. Federated learning (FL)-enabled intelligent manufacturing systems facilitate efficient data processing and intelligent decision making by deploying artificial intelligence models at the edge and integrating model aggregation mechanisms. However, anomalies occurring in the local devices on which the local models depend can propagate through the aggregation process, leading to model contamination and performance degradation, thereby compromising the task reliability of the entire system. To address this issue, this article proposes a reliability modeling approach for FL-enabled intelligent manufacturing systems (FL-IMSs). In the proposed model, we characterize the performance degradation process of the local model caused by terminal device failures and its impact on task reliability. This process includes the effect of data quality degradation triggered by device failures on model performance, as well as failure propagation caused by intermodel dependencies. Furthermore, to assess the impact of model performance variations on practical production tasks, a task-oriented reliability metric is introduced. Simulation and experimental results demonstrate that the proposed modeling approach effectively captures local model performance degradation and task reliability in FL-IMSs under terminal device failure conditions.