Federated deep learning for triple bottom line optimization in virtual refrigeration through simulation-based sustainable food management

Abstract

Global food systems are inefficient and result in the waste of at least one billion tonnes of food every year, contributing 8–10% of the anthropogenic greenhouse gas emissions, 1 trillion in economic losses and 600 million cases of food-borne diseases across the world. Although traditional smart refrigeration solutions have considerable potential, they are limited by the high price of hardware, the impossibility of scalability, and the lack of privacy due to the centralised processing of data. This study represents a new federated deep learning architecture that measures and maximizes the triple bottom line (environmental, economic, and health) effects by simulating virtual refrigeration. With the use of synthetic data of food-101 and multi-modal deep learning models such as CNN to identify food and LSTM to predict freshness, the framework operates 500 virtual household fridges, without violating privacy with federated learning. This system uses the mode of differential privacy (e = 1.0) to allow the collective upgrading of the model without exploiting delicate information. Findings reveal significant gains in all aspects of sustainability: AI performance had a 96.8% accuracy (25% improvement), user experience metrics had SUS scores of 87.3 (37% better) and security was at 91.5% attack resistance (51% better). The environmental advantages include 30–40% of food waste reduction, potentially minimizing CO₂ emissions by up to 200–300 kg per house. Economic effects will result in household savings of $500–800 per year in optimised inventory management. Health outcomes include minimised risk of foodborne illnesses by proper detection of spoilage and prevention of cross-contamination.