Federated learning in food research
The use of machine learning in food research is sometimes limited due to data sharing obstacles such as data ownership and privacy requirements. Federated learning is a technique to potentially alleviate these obstacles because it allows to train machine learning models locally, keeping the data pri...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
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| Series: | Journal of Agriculture and Food Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266615432500609X |
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| _version_ | 1849235909579374592 |
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| author | Zuzanna Fendor Bas H.M. van der Velden Xinxin Wang Andrea Jr. Carnoli Osman Mutlu Ali Hürriyetoğlu |
| author_facet | Zuzanna Fendor Bas H.M. van der Velden Xinxin Wang Andrea Jr. Carnoli Osman Mutlu Ali Hürriyetoğlu |
| author_sort | Zuzanna Fendor |
| collection | DOAJ |
| description | The use of machine learning in food research is sometimes limited due to data sharing obstacles such as data ownership and privacy requirements. Federated learning is a technique to potentially alleviate these obstacles because it allows to train machine learning models locally, keeping the data private and sharing only the learned parameters.In this review we investigate the use of federated learning in food research. First, we outline a framework that describes the variants of federated learning implementations. Then, we provide an overview of applications of federated learning in food research. Next, we discuss the performance of the models trained with federated learning, and reasons for the use of federated learning. Finally, we categorize the encountered federated learning applications within the federated learning framework. In the discussion, we highlight the knowledge gaps and discuss the potential novel applications.In this review we examined a total of 86 papers published between 2019 and 2024. The current applications encompass crop disease monitoring, yield prediction, quality assessment, and pesticide residue risk analysis. We observed the general trend of centralized horizontal federated learning, and identified the absence of vertical federated learning, federated transfer learning, and decentralized architectures as research gaps. |
| format | Article |
| id | doaj-art-3187f82e2c474fecb7d40e83a493c441 |
| institution | Kabale University |
| issn | 2666-1543 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj-art-3187f82e2c474fecb7d40e83a493c4412025-08-20T04:02:32ZengElsevierJournal of Agriculture and Food Research2666-15432025-10-012310223810.1016/j.jafr.2025.102238Federated learning in food researchZuzanna Fendor0Bas H.M. van der Velden1Xinxin Wang2Andrea Jr. Carnoli3Osman Mutlu4Ali Hürriyetoğlu5Corresponding author. Akkermaalsbos 2, Gebouw 123, 6708 WB, Wageningen, the Netherlands.; Wageningen Food Safety Research (WFSR), Part of Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the NetherlandsWageningen Food Safety Research (WFSR), Part of Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the NetherlandsWageningen Food Safety Research (WFSR), Part of Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the NetherlandsWageningen Food Safety Research (WFSR), Part of Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the NetherlandsWageningen Food Safety Research (WFSR), Part of Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the NetherlandsWageningen Food Safety Research (WFSR), Part of Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the NetherlandsThe use of machine learning in food research is sometimes limited due to data sharing obstacles such as data ownership and privacy requirements. Federated learning is a technique to potentially alleviate these obstacles because it allows to train machine learning models locally, keeping the data private and sharing only the learned parameters.In this review we investigate the use of federated learning in food research. First, we outline a framework that describes the variants of federated learning implementations. Then, we provide an overview of applications of federated learning in food research. Next, we discuss the performance of the models trained with federated learning, and reasons for the use of federated learning. Finally, we categorize the encountered federated learning applications within the federated learning framework. In the discussion, we highlight the knowledge gaps and discuss the potential novel applications.In this review we examined a total of 86 papers published between 2019 and 2024. The current applications encompass crop disease monitoring, yield prediction, quality assessment, and pesticide residue risk analysis. We observed the general trend of centralized horizontal federated learning, and identified the absence of vertical federated learning, federated transfer learning, and decentralized architectures as research gaps.http://www.sciencedirect.com/science/article/pii/S266615432500609XFederated learningFoodFood safetyData privacyMachine learningLiterature review |
| spellingShingle | Zuzanna Fendor Bas H.M. van der Velden Xinxin Wang Andrea Jr. Carnoli Osman Mutlu Ali Hürriyetoğlu Federated learning in food research Journal of Agriculture and Food Research Federated learning Food Food safety Data privacy Machine learning Literature review |
| title | Federated learning in food research |
| title_full | Federated learning in food research |
| title_fullStr | Federated learning in food research |
| title_full_unstemmed | Federated learning in food research |
| title_short | Federated learning in food research |
| title_sort | federated learning in food research |
| topic | Federated learning Food Food safety Data privacy Machine learning Literature review |
| url | http://www.sciencedirect.com/science/article/pii/S266615432500609X |
| work_keys_str_mv | AT zuzannafendor federatedlearninginfoodresearch AT bashmvandervelden federatedlearninginfoodresearch AT xinxinwang federatedlearninginfoodresearch AT andreajrcarnoli federatedlearninginfoodresearch AT osmanmutlu federatedlearninginfoodresearch AT alihurriyetoglu federatedlearninginfoodresearch |