Bridging Data Silos for Enhanced Predictive Maintenance: A Federated Learning Framework
Keywords:
Predictive Maintenance, Federated Learning, Data Silos, Industrial IoTAbstract
This paper presents a secure federated learning framework for industrial predictive maintenance that addresses data silos arising from privacy, competition, and regulatory constraints. The proposed approach combines PCA-based anomaly screening, LSTM-based self-encryption, client credibility scoring, CKKS homomorphic aggregation, and differential privacy to enable collaborative model training without sharing raw sensor data. Experiments on real-world vibration and acoustic datasets demonstrate that the proposed framework achieves failure detection performance close to centralized training, outperforming conventional federated baselines such as FedAvg and FedProx. Specifically, the model attains an average F1-score of 89.4 ± 0.5% and a remaining useful life prediction error (MAPE) of 8.7 ± 0.6%, while reducing communication overhead. Practical deployment considerations, including cryptographic overhead and hardware constraints, are critically discussed, highlighting trade-offs between security, efficiency, and scalability. The proposed framework provides a viable and privacy-preserving solution for predictive maintenance in Industry 4.0 environments.
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Copyright (c) 2025 Khalil Jahani, Behzad Moshiri, Babak Khalaj (Author)
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