Federated Learning in Manufacturing: A Systematic Review and Pathway to Industry 5.0

The aim of this research is to explore and evaluate the use of federated learning to tackle issues related to data privacy, data scarcity, data security, and anomaly detection in Industry 4.0 manufacturing settings and the transition towards industry 5.0. SCOPUS database has been used to systematically review existing literature on applying federated learning in Industry 4.0 manufacturing contexts. The identified literature is analyzed qualitatively using VOS viewer software and Microsoft Excel. Firstly, a bibliometric study following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines has been undertaken to provide insights into the progress of research on this topic. This analysis encompasses the examination of co-authorship patterns among countries as well as the analysis of keyword co-occurrence. Fur-thermore, a comprehensive analysis of the chosen articles (n=33) examines the specific research inquiries that will guide future research endeavors to bridge the existing gaps in knowledge and facilitate the implementation of federated learning in the context of Industry 4.0 manufacturing. The findings of this research demonstrate that the utilization of federated learning presents promising solutions for addressing challenges related to data isolation, data privacy, and efficient processing in the context of Industry 4.0 manufacturing. Furthermore, there is a growing number of emerging models that exhibit improved performance in these areas, with the aim of facilitating the implementation of Industry 5.0 in the manufacturing sector.