Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

In the 1940s, Claude Shannon developed the information theory focusing on quantifying the
maximum data rate that can be supported by a communication channel. Guided by this …

By exploiting the computing power and local data of distributed clients, federated learning
(FL) features ubiquitous properties such as reduction of communication overhead and …

Communication has been known to be one of the primary bottlenecks of federated learning
(FL), and yet existing studies have not addressed the efficient communication design …

Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange
among the participating devices while training for a common learning task. This way, FL can …

Federated learning (FL) is a machine learning model that preserves data privacy in the
training process. Specifically, FL brings the model directly to the user equipments (UEs) for …

Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

Machine learning and wireless communication technologies are jointly facilitating an
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …