This special issue will focus on information theoretic aspects of distributed coding and computing. While applications and platforms such as distributed learning, cloud storage and computing, content delivery networks, and distributed ledgers are increasingly popular, there is a tremendous need to evaluate the fundamental limits of the existing solutions and develop efficient approaches to run them. This is particularly important considering the growing list of constrains and requirements in terms available resources, scalability, privacy, security, fault tolerance, speed, accuracy, and verifiability. In this context, information theory and coding can play a major role in expanding and employing various tools and techniques to deal with those challenging tasks. This special issue aims to attract contributions investigating the fundamental limits of distributed information systems and developing efficient coding techniques to meet those limits, satisfying the essential constraints.

This 8th special issue will focus on exploring how new advances in information theory can impact future communication systems. Next generation wireless networks will incorporate a large number of devices, dense and intelligent antenna arrays, and operate in higher frequencies. New task-aware communication modalities, such as sensing, learning and inference, will accelerate the shift from human-to-human to machine-to-machine type communications. Accordingly, communication systems will be designed with capacity, latency and accuracy in mind. Increasingly complex communication tasks will need to be carried out on devices with energy and hardware constraints, but will also be able to take advantage of in-network storage and computation.

The CFP for JSAIT's 7th Special Issue. The focus of this special issue is on the applications of coding to the broad area of networking for efficient exploitation and delivery of data. Various coding techniques have been devised to tackle erasures and achieve fundamental limits of compression to recover a message with a fidelity criterion. Motivated by the research in this direction and a wide variety of applications at the intersection of distributed systems and networking, this special issue will focus on key aspects ranging from employment of coding for enhancing the efficiency of networking, protocols, computation and delivery in distributed systems, to maintaining consistency in updates and improving accessibility in distributed storage systems, as well as providing desired performance tradeoffs in terms of efficiency, delay and atomicity.

The CFP for JSAIT's 6th Special Issue. The intersection of information theory and computing has been a fertile ground for novel, relevant intellectual problems. Recently, coding-theoretic techniques have been designed to improve performance metrics in distributed computing systems. This has generated a significant amount of research that has produced novel fundamental limits, code deigns and practical implementations. The set of ideas leveraged by this new line of research is collectively referred to as coded computing. This special issue will focus on coded computing, including aspects such as tradeoffs between reliability, latency, privacy and security.

Sequential methods underpin many of the most powerful learning techniques, such as reinforcement learning, multi-armed bandits, online convex optimization, and active learning. Although many practical algorithms have been developed for sequential learning, there is a strong need to develop theoretical foundations and to understand fundamental limits. Herein lies an excellent opportunity for information theory to provide answers given its vast arsenal of versatile techniques. At the same time, sequential learning has already started to motivate new problems and insights in information theory and has led to new perspectives. This special issue seeks to fertilize new topics at the intersection of information theory and sequential, active, and reinforcement learning, promoting synergy along the way.