Blockchain-Empowered Federated Learning Applications in Smart Manufacturing: A Literature Review

Abstract Industry 4.0 and smart manufacturing increasingly require secure, decentralized, and collaborative data-driven systems. However, challenges such as data privacy, cybersecurity, trust among stakeholders, and heterogeneous data and data sources continue to limit the scalability and efficacy of conventional artificial intelligence (AI) and machine learning (ML) solutions. This review investigates the integration of blockchain (BC) and federated learning (FL) as a framework to address these challenges within the manufacturing domain. We aim to explore the existing application scenarios and technical approaches adopted for BC–FL integration in manufacturing, identify technical and organizational challenges, and uncover cross-domain innovations that may be adapted to industrial settings. Key identified applications in manufacturing include cybersecurity, predictive maintenance, supply chain optimization, digital twin (DT) systems, and quality control. Cross-domain insights offer promising strategies to support secure collaboration, improve model performance, and enhance trust in manufacturing environments. This review provides a reference for researchers and practitioners seeking to design secure, scalable, and collaborative AI systems in smart manufacturing environments using FL and BC technology.