Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …

Deep learning (DL), despite its enormous success in many computer vision and language
processing applications, is exceedingly vulnerable to adversarial attacks. We consider the …

How can machine learning help the design of future communication networks-and how can
future networks meet the demands of emerging machine learning applications? Discover the …

Thanks to the recent advances in processing speed, data acquisition and storage, machine
learning (ML) is penetrating every facet of our lives, and transforming research in many …

As data generation increasingly takes place on devices without a wired connection, Machine
Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have …

There is significant enthusiasm for the employment of Deep Neural Networks (DNNs) for
important tasks in major wireless communication systems: channel estimation and decoding …

Next-generation wireless networks are expected to support extremely high data rates and
radically new applications, which require a new wireless radio technology paradigm. The …

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …

Due to the growth of modern dataset size and the desire to harness computing power of
multiple machines, there is a recent surge of interest in the design of distributed machine …