Fed-GPD: Federated Graph Process Distillation for Anomaly Detection in Lights-Out Manufacturing

As an essential component of the Industrial Internet of Things (IIoT), lights-out manufacturing (LoM) relies heavily on the rapid detection of anomalies. However, LoM anomalies often arise from complex, cross-modal correlations, and traditional detection models struggle with the dual challenges of limited local data and stringent data privacy requirements, leading to poor generalization. To address these challenges, this paper introduces a novel Federated Graph Process Distillation framework (Fed-GPD) for multi-modal anomaly detection. Our approach first represents heterogeneous industrial data as unified graph structures to effectively model the underlying device relationships. We then propose a new graph knowledge distillation paradigm designed for Graph Neural Networks (GNNs) in a federated setting. Instead of merely distilling final predictions, we introduce two novel distillation mechanisms: 1) Neighborhood Aggregation Process Distillation (NAPD), which transfers the knowledge of how a model processes local neighborhood information at each GNN layer, and 2) Relational Knowledge Matrix Distillation (RKMD), which aligns the global understanding of node-to-node relationships learned by the models. These mechanisms are integrated into an asynchronous mentor-mentee architecture, enabling efficient and deep knowledge transfer from powerful, private mentor models to a lightweight, global mentee model. Simulation results on multiple real-world datasets demonstrate that Fed-GPD significantly outperforms existing federated and graph-based anomaly detection methods. Notably, our in-depth ablation studies validate the effectiveness of the proposed process distillation mechanisms, showing substantial improvements in model accuracy and communication efficiency.