Federated Learning: A Solution for Improving Anomaly Detection Accuracy of Autonomous Guided Vehicles in Smart Manufacturing

Autonomous Guided Vehicles (AGVs) play an instrumental role in smart manufacturing, particularly in the transport of materials and finished goods. Yet, unexpected anomalies with AGVs can pose safety risks and impede production. Conventional anomaly detection techniques are often hamstrung by data privacy concerns and an absence of centralized data access. Federated learning emerges as a viable alternative, championing distributed data analysis without the prerequisite of centralized data repositories. In this paper, we delve into the efficacy of federated learning in amplifying the precision of AGV anomaly detection within the ambit of smart manufacturing. Our empirical findings attest to the capability of federated learning in bolstering AGV anomaly detection accuracy, all while upholding the tenets of data privacy and security.