Federated learning in food research

TL;DR

This paper systematically reviews how federated learning is applied in food research, addressing data sharing challenges and highlighting current applications, gaps, and future directions in the field.

Contribution

It provides a comprehensive overview of federated learning applications in food research, identifying knowledge gaps and potential for future development.

Findings

41 papers reviewed on federated learning in food research

Current applications include quality assessment and fraud detection

Identified gaps in vertical, transfer, and decentralized federated learning

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 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.