Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts

Abstract Street vendors in developing regions often lack access to portable and affordable cold storage, leading to accelerated food spoilage, financial losses, and health risks. Traditional refrigeration solutions are bulky and costly, while manual freshness assessment is error-prone. This study proposes a smart vending cart integrating IoT sensors and federated learning (FL) to address these challenges, offering real-time environmental monitoring, freshness classification, and privacy-preserving data handling. The smart vending cart incorporates IoT sensors to monitor temperature, humidity, and gas emissions. A Peltier cooling module and a humidifier maintain optimal conditions. Machine learning models classify food freshness, while federated learning ensures vendor privacy by training models locally on each cart. The study explores nine federated learning approaches to train machine learning models across multiple carts without sharing raw data, thus preserving vendor privacy. The Stacking Ensemble approach outperformed all other methods, achieving the highest accuracy, F1-Score, and Cohen’s Kappa (0.99964), with the lowest log loss (0.0022). MetaLearning and Weighted Aggregation also demonstrated high performance but with marginally higher log loss values. Personalized models performed well in heterogeneous data environments but were less effective than ensemble methods. The developed smart vending cart system effectively reduces food spoilage and enhances vendor profitability through automated freshness classification and real-time environmental control. The integration of federated learning ensures privacy, while ensemble techniques improve robustness in resource-constrained settings, offering a scalable solution for street vendors.