Federated Learning for Predictive Maintenance: A Survey of Methods, Applications, and Challenges

Predictive maintenance plays a crucial role across diverse domains, ensuring operational efficiency and reliability. However, the increasing need for securing predictive maintenance, coupled with system vulnerabilities presents significant challenges. This paper explores the application of federated learning (FL) in predictive maintenance, focusing on its advantages in bandwidth efficiency, privacy preservation, reduced power consumption, lower latency and faster training times. We further investigate the potential and challenges of FL in predictive maintenance practices.