  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 papers in a
federated learning framework, highlights knowledge gaps, and discusses
potential applications. A total of 41 papers were included in the review. The
current applications include solutions to water and milk quality assessment,
cybersecurity of water processing, pesticide residue risk analysis, weed
detection, and fraud detection, focusing on centralized horizontal federated
learning. One of the gaps found was the lack of vertical or transfer federated
learning and decentralized architectures.
