New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global model by learning from multiple decentralized edge clients. Although FL methods offer several advantages, including scalability and data privacy, they also introduce some risks and drawbacks in terms of computational complexity in the case of heterogeneous devices. Internet of Things (IoT) devices may have limited computing resources, poorer connection quality, or may use different operating systems. This paper provides an overview of the methods used in FL with a focus on edge devices with limited computational resources. This paper also presents FL frameworks that are currently popular and that provide communication between clients and servers. In this context, various topics are described, which include contributions and trends in the literature. This includes basic models and designs of system architecture, possibilities of application in practice, privacy and security, and resource management. Challenges related to the computational requirements of edge devices such as hardware heterogeneity, communication overload or limited resources of devices are discussed.

With the intent to further increase production efficiency while making human the centre of the processes, human-centric manufacturing focuses on concepts such as digital twins and human–machine collaboration. This paper presents enabling technologies and methods to facilitate the creation of human-centric applications powered by digital twins, also from the perspective of Industry 5.0. It analyses and reviews the state of relevant information resources about digital twins for human–machine applications with an emphasis on the human perspective, but also on their collaborated relationship and the possibilities of their applications. Finally, it presents the results of the review and expected future works of research in this area.

Smart industry and the concept of Industry 4.0 bring new solutions, architectures, and technologies to industry and manufacturing. Some of these concepts have evolved. For example, Web 4.0, Operator 4.0, Society 5.0 and many other Technologies X.0. Among others, the Human Machine Interface has also changed from buttons and diodes in control rooms, through web visualization to cognitive and intelligent space. This paper describes this evolution and provides examples of solutions or case studies for every generation in this evolution.

In recent years, the main area of interest in the issue of influencing mental states of people is the impact of lighting on human beings, their wellbeing but also workplace productivity. This work discusses in detail the problem of positively influencing people using intelligent technologies, especially the role of the colors. We describe techniques and technologies needed to implement the case study of an intelligent lighting system. The system proposed can detect humans from an IP camera, find faces, and detect emotion. The main aim is to adjust the lights accordingly to the emotional result to improve the mood of people while taking into consideration the principles of color psychology and daytime. We have evaluated our case study solution in a real-world environment and collected the feedback from participants in the form of a questionnaire. Evaluation of participants’ wellbeing was based on their subjective statements. There were several ideas on further functionality extension which needs to be explored. Among them is including wearable devices to the proposed system, validate the emotional results according to them, but also determine the impact of an increasing number of users interacting with the system at the same time.

The onset of cloud computing brought with it an adoption of containerization—a lightweight form of virtualization, which provides an easy way of developing and deploying solutions across multiple environments and platforms. This paper describes the current use of containers and complementary technologies in software development and the benefits it brings. Certain applications run into obstacles when deployed on the cloud due to the latency it introduces or the amount of data that needs to be processed. These issues are addressed by edge intelligence. This paper describes edge intelligence, the deployment of artificial intelligence close to the data source, the opportunities it brings, along with some examples of practical applications. We also discuss some of the challenges in the development and deployment of edge intelligence solutions and the possible benefits of applying containerization in edge intelligence.