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13 June 2023 Federated learning for contamination detection and data privacy in food service industry (Conference Presentation)
Hamed Taheri Gorji, Mahdi Saeedi, Hossein Kashani Zadeh, Kaylee Husarik, Jianwei Qin, Diane E. Chan, Insuck Baek, Moon S. Kim, Alireza Akhbardeh, Stanislav Sokolov, Nicholas MacKinnon, Fartash Vasefi, Kouhyar Tavakolian
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Proceedings Volume PC12545, Sensing for Agriculture and Food Quality and Safety XV; PC1254505 (2023) https://doi.org/10.1117/12.2665343
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States
Abstract
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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Hamed Taheri Gorji, Mahdi Saeedi, Hossein Kashani Zadeh, Kaylee Husarik, Jianwei Qin, Diane E. Chan, Insuck Baek, Moon S. Kim, Alireza Akhbardeh, Stanislav Sokolov, Nicholas MacKinnon, Fartash Vasefi, and Kouhyar Tavakolian "Federated learning for contamination detection and data privacy in food service industry (Conference Presentation)", Proc. SPIE PC12545, Sensing for Agriculture and Food Quality and Safety XV, PC1254505 (13 June 2023); https://doi.org/10.1117/12.2665343
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KEYWORDS
Contamination
Data modeling
Industry
Evolutionary algorithms
Luminescence
Image segmentation
Imaging technologies
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