Abstract
Featured Application: The proposed federated learning framework can be applied in distributed smart manufacturing environments where multiple factories or production facilities collaboratively develop predictive maintenance models without sharing sensitive operational data. This approach is particularly useful for industrial networks involving geographically distributed plants, contract manufacturing partners, and multi-site production systems where data sovereignty and avoidance of raw data sharing are critical. Predictive maintenance enables early detection of machine failures and reduces unexpected production downtime. However, conventional approaches typically rely on centralized data collection and model training which introduce challenges related to data sovereignty, communication overhead and data ownership. To address these challenges, this research proposes a collaborative federated learning framework for predictive maintenance that can be deployed in distributed smart manufacturing systems. The proposed data-sovereign federated learning approach allows multiple factories to collaboratively train a machine failure prediction model while maintaining data locality. In the framework, each factory trains a local multilayer perceptron (MLP) model using its own machine operational data, while a central server aggregates local model parameters using the Federated Averaging (FedAvg) algorithm to construct a global predictive model. The proposed framework was evaluated using the publicly available AI4I 2020 predictive maintenance dataset, where multiple factories are simulated by partitioning the dataset into distributed clients. Experimental results show that the federated learning model achieves competitive performance compared to centralized machine learning baselines, attaining an accuracy of 97.17%, precision of 0.6000, recall of 0.5000, and F1-score of 0.5455. These results demonstrate that federated learning can enable effective predictive maintenance while maintaining data sovereignty in distributed manufacturing environments.
Recommended Citation
M. S. Ahmmed et al., "A Federated Learning Framework for Data-Sovereign Predictive Maintenance in Distributed Smart Manufacturing," Applied Sciences Switzerland, vol. 16, no. 10, article no. 5084, MDPI, May 2026.
The definitive version is available at https://doi.org/10.3390/app16105084
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Open Access
Keywords and Phrases
cyber–physical systems; federated learning; industrial AI; predictive maintenance; smart industry
International Standard Serial Number (ISSN)
2076-3417
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2026 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 May 2026
