Context-Aware Federated Learning for Enhanced Predictive Maintenance in Industrial IoT

Abstract

This paper investigates the application of context-aware federated learning (CAFL) to enhance predictive maintenance (PdM) in Industrial Internet of Things (IIoT) environments.  The inherent challenges of IIoT, including data heterogeneity, privacy concerns, and resource constraints, limit the effectiveness of traditional centralized machine learning approaches for PdM. CAFL addresses these challenges by enabling collaborative model training across distributed edge devices without directly sharing raw data.  We propose a novel CAFL framework that incorporates contextual information, such as operating conditions and environmental factors, to improve the accuracy and robustness of PdM models. The framework is evaluated using a simulated IIoT environment with diverse machine types and operating conditions.  Experimental results demonstrate that CAFL significantly outperforms traditional federated learning and centralized learning approaches in terms of prediction accuracy, model generalizability, and data privacy preservation.  The paper concludes by discussing the implications of CAFL for future IIoT applications and outlining potential avenues for further research.