Federated Learning for Privacy-Preserving Predictive Maintenance inMulti-Site Industrial IoT Networks: Architecture, Convergence Analysis,and Benchmark Evaluation
Keywords:
Federated Learning, Industry 4.0, RUL Prediction, Industrial IoT, Predictive MaintenanceAbstract
Predictive maintenance (PdM) in multi-site industrial settings requires training machine learning models on condition monitoring data that is distributed across facilities operated by competing organisations, making
centralised data aggregation infeasible due to privacy, intellectual property, and regulatory constraints. Federated learning (FL) offers a paradigm-shift solution by enabling collaborative model training without raw data exchange: each site trains locally and shares only model gradient updates with a central aggregation server. This paper presents FedPdM, a federated learning architecture tailored for industrial IoT predictive maintenance, incorporating: (i) FedAvg-based global aggregation with adaptive client
weighting proportional to local dataset quality; (ii) a non-IID robustness mechanism using Scaffold variance reduction to address heterogeneous failure mode distributions across sites; and (iii) differential privacy noise injection at the gradient level to provide formal ε-DP guarantees. FedPdM is evaluated on the CMAPSS turbofan engine degradation benchmark and a proprietary 12 site industrial compressor dataset, achieving Remaining Useful Life prediction RMSE of 14.3 cycles (CMAPSS FD001 subset) comparable to centralised baseline RMSE of 13.8 cycles while providing (ε=2.1, δ=10⁻⁵) differential privacy at a ommunication overhead of 2.3 MB per round. Convergence is achieved in 38 rounds across 12 heterogeneous clients, confirming practical viability for real industrial multi-site deployment.
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