Federated Learning and its Role in Predictive Maintenance

Abstract Predictive maintenance (PdM) has emerged as a cornerstone of Industry 4.0, enabling organizations to anticipate equipment failures, optimize maintenance schedules, and reduce operational costs. The convergence of artificial intelligence (AI), the Industrial Internet of Things (IIoT), and advanced analytics has facilitated the transition from reactive and preventive maintenance to predictive and even prescriptive strategies. Within this context, federated learning (FL) offers a transformative approach to PdM by enabling collaborative model training across distributed and privacy-sensitive data sources without the need to centralize raw data. This chapter explores the integration of FL into PdM systems, highlighting its potential to improve sustainability and resilience in industrial maintenance. The discussion extends beyond technical mechanisms to include enabling technologies such as edge and fog computing, IIoT platforms, and blockchain. Furthermore, it addresses the sociotechnical implications of privacy-preserving AI in industrial environments, focusing on its role in supporting worker expertise and ensuring sustainable practices. Case studies, key challenges, and future research directions are presented to provide a comprehensive view of the field.