Digital services, including healthcare, among others, have recently seen a massive volume of complicated data that arrives rapidly due to a rapid increase in the number of smart devices. Different areas of the healthcare sector and other information systems create huge amounts of data, including data from hospitals and healthcare service providers. With technological advancements, there is significant potential to use this data to change digital services and healthcare.

In recent years, the digital world of big data and the Internet of Things (IoT) has seen an incredible increase in data, from 1.2 zettabytes to almost 67 zettabytes. It is projected that more than 97 zettabytes of data will be produced and spent in the world by the end of 2022. In addition, the worldwide market for connected medical devices is predicted to grow from $41 billion in 2017 to $158 billion in 2022. Therefore, the growth rate in big data and the IoT is far exceeding the computational services needed to process these quantities of data. Edge computing is transforming health care by putting large data processing and storage closer to the source, allowing the application of game-changing technologies like IoT and artificial intelligence (AI). Edge computing, cloud, IoT, and other emerging technologies enriched by federated learning applications can help in processing such amount of huge data.

The aim of this Special Issue is to look for contributions that attempt to integrate federated learning and AI techniques into the design of algorithms for smart information systems and healthcare. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Machine learning (ML) techniques and algorithms for edge intelligence and computing services
• Partitioning healthcare services across IoT, edge, and cloud environments
• Secure and reliable IoT, edge, and cloud services for digital healthcare
• Intelligent computing, code offloading, and edge processing
• Ecosystems for improving data processing speed and performance
• Applications of IoT, edge, fog, and machine learning in healthcare, intelligent agriculture, and smart cities
• Optimisation, particle swarm, artificial intelligence, and swarm intelligence
• Medical cyber-physical systems
• Methods for processing healthcare data in edge and cloud environments
• Computation, data, and network management to facilitate IoT, edge, and cloud integration in the healthcare sector