The new computing paradigm of cyber-physical systems (CPS) includes intelligent connected devices, vehicles, intelligent health care systems, smart farming, and intelligent logistics that constantly generate tons of heterogeneous data and confidential data. These datasets need enormous computing resources with real-time data processing capabilities and privacy preservation. Therefore, the edge computing paradigm provides fast computation with intelligent decentralized computing resources close to the connected end devices or models of the mobile edge network. Thus, the edge computing paradigm is becoming an ideal platform for efficient analysis and processing for CPS.

Therefore, many researchers have started implementing intelligent decentralized computing to leverage the next generation of CPS, termed intelligent decentralized computing paradigms using federated learning (FL) techniques for smart CPS. The intelligent decentralized computing paradigm comprises FL and explainable artificial intelligence (XAI), distributed and software-intensive systems, and models. Learning models may effectively and autonomously solve critical challenges in service latency, energy consumption, security, privacy, and reliability during the integration of smart CPS with edge computing intelligence paradigms. The primary objective of intelligent decentralized enabled FL is to leverage fast computation and solve the issues and challenges mentioned above on connected edge devices (smart devices) and other models with edge computing paradigms to assist smart CPS and applications. This computational revolution will be fuelled by and integrated with computation at individual edges or network nodes for more effective assistance with smart CPS. This will be characterized by deep, complex intertwining computing processes with innovative cyber devices and interconnected components for fast computation and extensive control using intelligent techniques and models including FL XAI, QML, multimodal deep Learning (DL), and FL and dynamic physical components that include human activities, mechanical parts, and the surrounding environment.

The primary objective of this Special Issue is to explore and built models and examine the security, privacy preservation, and trust difficulties of smart CPS using an intelligent decentralized computing paradigm. This Special Issue will also explore the role of edge intelligence computing as an integrating and enabling platform for this paradigm. We welcome both original research and review papers.

Potential topics include but are not limited to the following:

• Multimodal ML modelling for edge mobile computing
• DL Based security verification and analysis of privacy, security and trustworthiness in intelligent edge intelligence computing for smart CPS and IoT
• Evaluation, detection and prevention of attacks in edge intelligence for smart CPS and industry 4.0
• ML, DL and FL based security solution in edge intelligence for smart CPS and IoT
• Blockchain and FL enabled for fully decentralized collaboration of edge intelligence for smart CPS and IoT
• Human and edge intelligence collaboration and interaction in high-level security for smart CPS and smart manufacturing• Smart production and trustworthy smart CPS
• Resource management in edge intelligence-based decentralized smart CPS
• FL based implementation, design, and operation of edge intelligence platforms for intelligent decentralized smart CPS
• Blockchain enabled FL for big data managing for smart CPS
• Resilience and reliability in edge intelligence-based smart CPS
• Self-adaptation and self-configuration in smart CPS
• Real-time data analytics for smart CPS
• Digital Twins, Blockchain, FL for smart CPS