Federated Learning Framework for Privacy-Preserving Industrial IOT Applications

Abstract

The proliferation of Industrial Internet of Things (IIoT) devices generates vast quantities of sensitive operational data that can drive predictive maintenance and process optimization through machine learning. However, centralized data aggregation raises critical concerns regarding data privacy, network bandwidth, and regulatory compliance. This paper proposes FedIIoT, a federated learning framework specifically designed for industrial IoT environments, incorporating adaptive client selection, gradient compression, and local differential privacy mechanisms. The framework was evaluated on three industrial datasets: bearing fault detection (CWRU), gas turbine anomaly classification (NASA C-MAPSS), and industrial process quality prediction (Tennessee Eastman). FedIIoT achieved 94.6% average accuracy across tasks within 0.7% of centralized training while reducing communication costs by 78% compared to standard federated averaging (FedAvg). Under a differential privacy budget of ε = 2.0, the framework maintained 91.2% accuracy, outperforming FedAvg with differential privacy by 5.4 percentage points. These results demonstrate that privacy-preserving distributed learning is practically viable for industrial applications without sacrificing model performance.