We study the distributed mean estimation (DME) problem under privacy and communication constraints in the local differential privacy (LDP) and multi-message shuffled (MMS) privacy frameworks. The DME has wide applications in both federated learning and analytics. We propose a communication-efficient and differentially private algorithm for DME of bounded $\ell _{2}$ -norm and $\ell _{\infty }$ -norm vectors. We analyze our proposed DME schemes showing that our algorithms have order-optimal privacy-communication-performance trade-offs.

In the problem of quickest change detection, a change occurs at some unknown time in the distribution of a sequence of random vectors that are monitored in real time, and the goal is to detect this change as quickly as possible subject to a certain false alarm constraint. In this work we consider this problem in the presence of parametric uncertainty in the post-change regime and controlled sensing.

Sensing has emerged as a key feature of modern wireless communication systems and networks. Traditionally, advances in the design, optimization and deployment of sensing and communication systems have evolved somewhat independently of each other.

To support the fast growth of IoT and cyber physical systems, as well as the advent of 6G, there is a need for communication and networking models that enable more efficient modes for machine-type communications. This calls for a departure from the assumptions of classical communication theoretic problem formulations as well as the traditional network layers. This new paradigm is referred to as goal or task-oriented communication, and is relevant also in part of the emerging area of semantic communications.

Causal determinism, is deeply ingrained with our ability to understand the physical sciences and their explanatory ambitions. Besides understanding phenomena, identifying causal networks is important for effective policy design in nearly any avenue of interest, be it epidemiology, financial regulation, management of climate, etc. This special issue covers several areas where causal inference research intersects with information theory and machine learning.

Transverse-read is a novel technique to detect the number of ‘1’s stored in a domain wall memory, also known as racetrack memory, without shifting any domains. Motivated by the technique, we propose a novel scheme to combine transverse-read and shift-operation such that we can reduce the number of shift-operations while still achieving high capacity. We also show that this scheme is helpful to correct errors in domain wall memory. A set of valid-words in this transverse-read channel is called a transverse-read code.

We develop a data-driven framework to identify the interconnections between firms using an information-theoretic measure. This measure generalizes Granger causality and is capable of detecting nonlinear relationships within a network. Moreover, we develop an algorithm using recurrent neural networks and the aforementioned measure to identify the interconnections of high-dimensional nonlinear systems. The outcome of this algorithm is the causal graph encoding the interconnections among the firms.

We consider a semantics-aware communication system, where timeliness is the semantic measure, with a source which maintains the most current version of a file, and a network of $n$ user nodes with the goal to acquire the latest version of the file. The source gets updated with newer file versions as a point process, and forwards them to the user nodes, which further forward them to their neighbors using a memoryless gossip protocol.