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Privacy-Preserving Industrial IoT Data Analysis Using Federated Learning in Multi-Cloud Environments
- 1 Electronics & Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
- 2 Business Analytics, Trine University, AZ, USA; Electronics & Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
- 3 Business Intelligence, Engineering School of Information and Digital Technologies, Villejuif, France
- 4 Electronics and Communications Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
* Author to whom correspondence should be addressed.
Abstract
The demand for storage and computing of massive amounts of industrial IoT data has led to increasing concerns about data privacy and security in multi-cloud environments. While federated learning enables collaborative model training without sharing raw data, existing solutions lack comprehensive privacy protection mechanisms suitable for industrial scenarios. This paper proposes a privacy-preserving federated learning framework specifically designed for industrial IoT data analysis across multiple clouds. The framework incorporates a novel differential privacy mechanism with adaptive noise injection to protect local model updates, while a Byzantine-resilient secure aggregation protocol ensures reliable model convergence under malicious attacks. A distributed key management system enables secure cross-cloud communication without centralized trust. Extensive experiments on real industrial datasets across three major cloud platforms demonstrate the effectiveness of our approach. The proposed method achieves 93.5% model accuracy while maintaining strong privacy guarantees, showing 15% improvement in privacy protection and 30% reduction in communication overhead compared to existing solutions. The system supports efficient scaling across multiple cloud providers while ensuring consistent privacy protection. The evaluation results confirm that our framework provides a practical solution for privacy-preserving industrial data analysis in multi-cloud environments.
Keywords
Federated Learning, Industrial IoT, Privacy Preservation, Multi-Cloud Computing
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Cite this article
Wan,W.;Guo,L.;Qian,K.;Yan,L. (2025). Privacy-Preserving Industrial IoT Data Analysis Using Federated Learning in Multi-Cloud Environments. Applied and Computational Engineering,141,7-16.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Mechatronics and Smart Systems
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