We study a distributed machine learning problem carried out by an edge server and multiple
agents in a wireless network. The objective is to minimize a global function that is a sum of …

End-to-end learning of communications systems is a fascinating novel concept that has so
far only been validated by simulations for block-based transmissions. It allows learning of …

We focus on a distributed learning problem in a communication network, consisting of
distributed nodes and a central parameter server (PS). The PS is responsible for performing …

We consider a distributed learning problem in a wireless network, consisting of distributed
edge devices and a parameter server (PS). The objective function is a sum of the edge …

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 …

Federated edge learning is envisioned as the bedrock of enabling intelligence in next-
generation wireless networks, but the limited spectral resources often constrain its …

In distributed optimization and machine learning, multiple nodes coordinate to solve large
problems. To do this, the nodes need to compress important algorithm information to bits so …

We consider distributed machine learning at the wireless edge, where a parameter server
builds a global model with the help of multiple wireless edge devices that perform …

State-of-the-art performance for many edge applications is achieved by deep neural
networks (DNNs). Often, these DNNs are location-and time-sensitive, and must be delivered …

The practical realization of end-to-end training of communication systems is fundamentally
limited by its accessibility of the channel gradient. To overcome this major burden, the idea …