Privacy-Preserving Industrial IoT Data Analysis Using Federated Learning in Multi-Cloud Environments
Weixiang Wan, Lingfeng Guo, Kun Qian, Lei Yan · Applied and Computational Engineering · 2025
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.