Federated Learning-Driven IoT System for Automated Freshness Monitoring in Resource-Constrained Vending Carts

Abstract

Food spoilage in vending carts leads to economic losses and health risks, particularly in resource-constrained environments. This study proposes a Federated Learning-driven IoT System to enable automated freshness monitoring in vending carts while preserving data privacy. The research investigates five key aspects: (1) the role of IoT sensors in environmental monitoring, (2) the effectiveness of machine learning models in freshness classification, (3) the capability of federated learning in ensuring data privacy, (4) comparative performance of federated learning techniques, and (5) the impact of ensemble methods on system robustness. Data from 2020 to 2023, collected from vending carts equipped with IoT sensors, were analyzed using machine learning and federated learning frameworks. The findings demonstrate that advanced IoT sensor integration improves environmental monitoring accuracy, ensemble-based machine learning models enhance freshness classification, and federated learning effectively maintains data privacy. Furthermore, stacking ensemble methods outperform other federated learning techniques, providing higher accuracy and robustness. The results emphasize the potential of federated learning in optimizing freshness monitoring while ensuring privacy and reliability, addressing critical gaps in food safety and IoT-based monitoring systems.