The food service industry must keep premises clean and free of foodborne pathogens that can be harbored in biofilms and organic residues. These may cause foodborne infections, endangering consumers and service providers. New fluorescence technology with advanced artificial intelligence algorithms can be a solution for detecting invisible contamination problems. However, improving algorithms requires access to data, raising concerns about data privacy and potential leaks of sensitive data. We present federated learning, a decentralized privacy-preserving method, to train algorithms for precisely detecting contamination in food preparation facilities and improving cleanliness while providing data privacy assurance for clients in the food-service industry.
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