Federated Explainable Artificial Intelligence Framework for Privacy-Preserving Industrial Risk Assessment in Smart Manufacturing Ecosystems

Abstract

The rapid adoption of Industry 4.0 technologies, Industrial Internet of Things (IIoT), cyber-physical systems, and intelligent automation has significantly transformed modern smart manufacturing ecosystems. However, the increasing interconnectivity of industrial devices and distributed production infrastructures has introduced critical challenges related to industrial risk assessment, data privacy, cybersecurity, model transparency, and trustworthy decision-making. Traditional centralized Artificial Intelligence (AI)-based risk assessment frameworks often require extensive data sharing, thereby exposing sensitive industrial information and increasing vulnerability to privacy breaches and cyberattacks. Furthermore, the lack of explainability in many AI-driven industrial systems limits stakeholder trust, regulatory compliance, and operational interpretability in safety-critical manufacturing environments.To address these challenges, this paper proposes a Federated Explainable Artificial Intelligence (FXAI) framework for privacy-preserving industrial risk assessment in smart manufacturing ecosystems. The proposed framework integrates Federated Learning (FL) with Explainable Artificial Intelligence (XAI) techniques to enable decentralized collaborative model training without exposing raw industrial data while simultaneously providing transparent and interpretable risk predictions. The framework employs secure edge-based learning, encrypted model aggregation, anomaly-aware risk analytics, and explainability mechanisms such as SHAP and attention-based interpretation to enhance industrial decision support and operational trustworthiness. In addition, blockchain-assisted verification and secure communication mechanisms are incorporated to improve data integrity, decentralized trust management, and tamper-resistant industrial collaboration.The proposed architecture is designed to support real-time industrial hazard prediction, predictive maintenance, cybersecurity threat detection, equipment failure analysis, and operational risk optimization across distributed manufacturing units. Experimental analysis demonstrates that the proposed FXAI framework achieves improved privacy preservation, prediction accuracy, scalability, interpretability, and communication efficiency compared with conventional centralized industrial AI models.The framework also enhances regulatory compliance, resilience, and secure industrial intelligence sharing while reducing data exposure risks in heterogeneous smart factory environments.The proposed research contributes toward the development of trustworthy, secure, explainable, and privacy-aware intelligent manufacturing systems capable of supporting next-generation autonomous industrial risk management in decentralized smart manufacturing ecosystems.