Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where data privacy, security, and efficient utilization of distributed resources are paramount. The essence of FL in IIoT lies in its ability to learn from diverse, distributed data sources without requiring central data storage, thus enhancing privacy and reducing communication overheads. However, despite its potential, several challenges impede the widespread adoption of FL in IIoT, notably in ensuring interpretability and robustness. This article focuses on enabling trustworthy FL in IIoT by bridging the gap between interpretability and robustness, which is crucial for enhancing trust, improving decision-making, and ensuring compliance with regulations. Moreover, the design strategies summarized in this article ensure that FL systems in IIoT are transparent and reliable, vital in industrial settings where decisions have significant safety and economic impacts. The case studies in the IIoT environment driven by trustworthy FL models are provided, wherein the practical insights of trustworthy communications between IIoT systems and their end users are highlighted.

The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intelligent, seamlessly interconnected ecosystems. However, achieving highly reliable IIoT can be hindered by factors such as the cost of installing large numbers of sensors, limitations in retrofitting existing systems with sensors, or harsh environmental conditions that may make sensor installation impractical. Soft (virtual) sensing leverages mathematical models to estimate variables from physical sensor data, offering a solution to these challenges. Data-driven and physics-based modeling are the two main methodologies widely used for soft sensing. The choice between these strategies depends on the complexity of the underlying system, with the data-driven approach often being preferred when the physics-based inference models are intricate and present challenges for state estimation. However, conventional deep learning models are typically hindered by their inability to explicitly represent the complex interactions among various sensors. To address this limitation, we adopt Graph Neural Networks (GNNs), renowned for their ability to effectively capture the complex relationships between sensor measurements. In this research, we propose physics-enhanced GNNs, which integrate principles of physics into graph-based methodologies. This is achieved by augmenting additional nodes in the input graph derived from the underlying characteristics of the physical processes. Our evaluation of the proposed methodology on the case study of district heating networks reveals significant improvements over purely data-driven GNNs, even in the presence of noise and parameter inaccuracies.

In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation offloading decision and multichannel access policy with corresponding signaling. Specifically, the base station and industrial Internet of Things mobile devices are reinforcement learning agents that need to cooperate to execute their computation tasks within a deadline constraint. We adopt an emergent communication protocol learning framework to solve this problem. The numerical results illustrate the effectiveness of emergent communication in improving the channel access success rate and the number of successfully computed tasks compared to contention-based, contention-free, and no-communication approaches. Moreover, the proposed task offloading policy outperforms remote and local computation baselines.

Advanced persistent threats (APTs) have novel features such as multi-stage penetration, highly-tailored intention, and evasive tactics. APTs defense requires fusing multi-dimensional Cyber threat intelligence data to identify attack intentions and conducts efficient knowledge discovery strategies by data-driven machine learning to recognize entity relationships. However, data-driven machine learning lacks generalization ability on fresh or unknown samples, reducing the accuracy and practicality of the defense model. Besides, the private deployment of these APT defense models on heterogeneous environments and various network devices requires significant investment in context awareness (such as known attack entities, continuous network states, and current security strategies). In this paper, we propose a few-shot multi-domain knowledge rearming (FMKR) scheme for context-aware defense against APTs. By completing multiple small tasks that are generated from different network domains with meta-learning, the FMKR firstly trains a model with good discrimination and generalization ability for fresh and unknown APT attacks. In each FMKR task, both threat intelligence and local entities are fused into the support/query sets in meta-learning to identify possible attack stages. Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost. Compared to multiple model replacement strategies, the FMKR provides a faster response to attack behaviors while consuming less scheduling cost. Based on the feedback from multiple real users of the Industrial Internet of Things (IIoT) over 2 months, we demonstrate that the proposed scheme can improve the defense satisfaction rate.

The University's research, titled'A Multi-Layer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT,' details the cutting-edge AI- and deep learning-based malware detection system that looks to safeguard the Industrial Internet of Things from cyber-attacks. In recent years, the Industrial Internet of Things has gained traction because of its ability to create novel communication networks between different aspects of industry and power the evolution to Industry 4.0. The Industrial Internet of Things is powered by wireless 5G connectivity, and AI is able to examine and resolve critical problems that enhance the operational performance of industries, such as manufacturing and healthcare. Whereas the Internet of Things is user-centric, connecting televisions, voice assistants, and refrigerators, for example, the Industrial Internet of Things optimises the health, safety, and efficiency of larger systems, connecting hardware with software and performing data analysis to provide insights in real-time. Despite the various benefits of the Industrial Internet of Things, it also carries a range of vulnerabilities, including security threats such as attacks that disturb the network or drain resources.

The Industry 4.0 market is poised to grow significantly. The increasing adoption of IoT in the digital transformation of manufacturing and related industries, the rise of industrial robotics and the proportionally higher spend in the Industrial Internet of Things are just some contributing factors. While we are still in the early days of Industry 4.0 and challenges remain on many fronts such as the integration of IT and OT, data capabilities, implementation challenges, guidelines and strategic capacities, skills, culture, standards and the maturity/readiness levels on the path from sheer optimization/automation to real transformation, Industry 4.0 is also driven by myriad challenges in the supply chain and customer expectations. The Industry 4.0 technology market is forecasted to reach $152.31 In an ongoing quest to deliver the value of Industry 4.0, it is certainly also boosted by national and supra-national pushes in a changing geopolitical industrial ecosystem, further driven by some of the larger players and alliances in the industry.

Conferences, media, vendors, automation industry consultants, business consultants, and even politicians are discussing and making presentations about how the Internet of Things (IoT) and Industry 4.0 are creating a revolution in manufacturing. I am convinced we are at a juncture of major industrial automation changes driven by technology advancements. The digital revolution of business functions, including accounting, supply chain, human resources, procurement, customer services, business intelligence, and distribution management, has been refined over multiple generations. In contrast, the industrial and process automation industries have not transformed at the same rate. They must be digitized now for manufacturers to compete. At the end of this article I have the results of a small survey of readers that may be interesting.

The 23rd China Hi-Tech Fair (CHTF), which attracts global attention, officially opens on December 27. The 23rd CHTF China Smart City Expo, which is the focus of attention at the CHTF, kicks off simultaneously at Hall 4 of Shenzhen Convention and Exhibition Center and at Hall 11 of Shenzhen World Exhibition & Convention Center. With the theme of "Data Driven, Smart Empowered", this CHTF China Smart City Expo, which is jointly organized by the State Information Center and Asia Digital Group, brings together innovative application technologies and solutions in the field of smart city including forward-looking 5G, industrial Internet, Internet of Things, big data, blockchain, digital industry, artificial intelligence, and smart technology, presenting the new development path and new pattern of future smart city. China is the world leader in digital infrastructure construction, with a well-defined system for open data sharing, and a sound system for institutional support. At the macro level, "accelerating digital development and building a digital China" is listed as an independent section in the outline of the "14th Five-Year Plan", which points out the need to develop seven key industries of the digital economy: cloud computing, big data, Internet of Things, industrial Internet, blockchain, artificial intelligence, and virtual reality as well as augmented reality.

The crucial component making smart technologies possible – from something as small as a ring to as large as an entire city – is the IoT. Although there are varying definitions, the term IoT is mainly used for previously'dumb' devices that didn't have an Internet connection, but that now communicate with the network independently of human action. For this reason, a smartphone isn't explicitly defined as an IoT device – although it's crammed with sensors. A connected refrigerator or microwave oven however is. Nowadays, these smart technology devices devices include billions of objects of all shapes and sizes – coffee machines, lightbulbs, driver-less trucks, wearable fitness devices, jet engines and children's smart toys – all equipped with sensors and communicating data through the Internet.

In recent years, Google's autonomous cars have logged thousands of miles on American highways and IBM's Watson trounced the best human Jeopardy! Digital technologies--with hardware, software, and networks at their core--will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human. In The Second Machine Age MIT's Erik Brynjolfsson and Andrew McAfee--two thinkers at the forefront of their field--reveal the forces driving the reinvention of our lives and our economy. As the full impact of digital technologies is felt, we will realize immense bounty in the form of dazzling personal technology, advanced infrastructure, and near-boundless access to the cultural items that enrich our lives. What is the Internet of Things?