FedPdM: Privacy-Preserving Federated Learning for Remaining Useful Life Prediction Across Heterogeneous Industrial Systems

• We have introduced a unified DP-FL framework for
multi-domain RUL prediction that combines FedProx
with formal (ε, δ)-differential privacy;
• We have conducted a cross-architecture benchmark of five
deep learning models under federated non-IID conditions
on three industrial datasets;
• We have proposed three new DP-FL evaluation metrics,
plus evidence that personalized federation consistently
closes the gap to the centralized upper bound.