A systematic literature review on the applications of federated learning and enabling technologies for livestock management

• A systematic review of recent works on federated learning in livestock industries. • Lack of studies on federated learning and enabling technologies in livestock sector. • Need to address technical and non-technical issues for use of federated learning. • Opportunities for federated learning and enabling technologies in livestock sector. This paper conducts a systematic review of the literature on the application and integration of federated learning, blockchain technology, and the Internet of Things (IoT) in livestock management. To achieve this objective, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was applied, guiding the review process to ensure transparency and comprehensiveness. Extensive searches were carried out from five academic databases, such as ScienceDirect, IEEE Xplore, Springer, Multidisciplinary Digital Publishing Institute (MDPI), and Association for Computing Machinery (ACM). A total of 1,259 articles were reviewed and 20 articles were finally selected for analysis. The study reveals that there is limited research on the integration of federated learning, blockchain technology, and the IoT in the livestock sector. However, these technologies have application in the sector for improving efficiency, optimizing crop and animal management, and promoting environmentally sustainable practices. The study suggested that several key issues need to be considered for using these technologies such as the protection of data privacy, the management of information diversity, and restrictions on connectivity, as well as the need to motivate cooperation and commitment between different stakeholders in the sector. This study provides a reference for researchers on the usefulness of these technologies for increasing efficiency and transparency in livestock management.