The proliferation of data silos poses a significant impediment to the advancement of machine learning applications. The traditional approach of centralized data learning is becoming increasingly impractical in certain domains, primarily due to escalating concerns over data privacy and security. Particularly in the manufacturing sector, the integration of Federated Learning (FL) presents a promising avenue for safeguarding collaborative data mining efforts across a network of distributed manufacturers. This paper offers an in-depth review of research about FL in the realms of smart manufacturing and product lifecycle management. We elucidate the imperative need for FL applications from a socio-technical systems perspective, underscoring the interplay between societal and technological factors. Subsequently, we delve into the categorization of FL methodologies and their pivotal enablers, contextualized within the framework of manufacturing engineering. This paper further presents a comprehensive overview of FL applications, complemented by an analysis of the key performance metrics that are germane to the manufacturing industry. In conclusion, we engage in a discourse on the technical challenges, societal barriers, and prospective research trajectories for FL. Our discussion is anchored towards the emerging paradigm of Industry 5.0, which envisions a future where resilient, human-centric, and sustainable manufacturing systems are seamlessly integrated with cutting-edge digital technologies.