Privacy-Preserving and Trustworthy AI for Industrial 4.0 and Beyond

Dear Colleagues,

The advent of Industry 4.0 is transforming manufacturing, logistics, energy and other industrial sectors through the integration of cyber-physical systems, large-scale data analytics, edge computing, connectivity and artificial intelligence (AI). In parallel, the rise of Industry 5.0 and beyond emphasizes human-machine collaboration, sustainability, resilience and trust. In this evolving landscape, ensuring the privacy, robustness, transparency, and trustworthiness of AI systems has become a pivotal challenge and enabler for industrial adoption. This Special Issue calls for original research and reviews on methodologies, frameworks, systems and empirical studies that advance the state of the art in privacy-preserving and trustworthy AI tailored to industrial settings—including federated/edge learning, multi-stakeholder data ecosystems, secure IoT/IIoT networks, explainable and auditable AI, safe deployment in autonomous industrial operations, and lifecycle assurance of trusted AI systems. We welcome contributions that span foundational theory (e.g., cryptographic, statistical or systems guarantees), algorithmic design (e.g., federated learning with privacy/robustness, transparent ML in industrial contexts), system implementation (e.g., deployment in smart factories, energy grids, supply-chain networks) and real-world case studies (e.g., industrial pilot results, regulatory or governance aspects). Our aim is to provide a definitive venue linking the demands of next-generation industrial ecosystems with trustworthy AI technologies, thus enabling secure, sustainable and intelligent industrial transformation.

Dr. Youyang Qu
Dr. Chenhao Xu
Dr. Yao Zhao
Guest Editors

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