Challenges and Future Directions in Federated Learning for Rul Estimation in Power Electronics: Insights From Recent Research

Federated Learning (FL) is a decentralized training framework that allows participants to build shared models without exchanging raw data, effectively addressing privacy constraints in industrial use cases. FL has been increasingly investigated in power electronics for Remaining Useful Life (RUL) estimation to enhance predictive maintenance. This paper explores the key challenges associated with FL for RUL estimation, including data heterogeneity, communication constraints, and model aggregation complexities. Furthermore, we discuss future directions, such as advanced aggregation techniques, hybrid physics-informed models, and standardization of FL benchmarks to improve their applicability. Insights are drawn from recent research, including a case study on federated RUL estimation in power electronics [6].