June 11, 2024, 4:47 a.m. | Zuzanna Fendor, Bas H. M. van der Velden, Xinxin Wang, Andrea Jr. Carnoli, Osman Mutlu, Ali H\"urriyeto\u{g}lu

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.06202v1 Announce Type: new
Abstract: Research in the food domain is at times limited due to data sharing obstacles, such as data ownership, privacy requirements, and regulations. While important, these obstacles can restrict data-driven methods such as machine learning. Federated learning, the approach of training models on locally kept data and only sharing the learned parameters, is a potential technique to alleviate data sharing obstacles. This systematic review investigates the use of federated learning within the food domain, structures included …

abstract arxiv cs.lg data data-driven data sharing domain federated learning food important machine machine learning obstacles ownership privacy regulations requirements research training training models type while

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