Optimized pathogen environmental monitoring program in food processing facilities through reinforcement learning and privatized federated learning algorithms
The key challenges in pathogen environmental monitoring programs stem from the high cost in testing and experimentation, the high risk in contamination and outbreak, and the reluctance of individual facilities to share data due to privacy and liability concerns. This project uses Listeria monocytogenes contamination in food processing facilities as a model to develop new digital-twin models augmented with privacy-guaranteed machine learning solutions for food safety assessment. This proposed integrative framework will provide optimized allocation of testing resources, risk-averse prediction of effective corrective measures, and privacy guarantees to incentivize data sharing among stakeholders and be used as a model for future food safety systems.
