We consider a distributed learning problem over multiple access channel (MAC) using a
large wireless network. The computation is made by the network edge and is based on …

We study federated machine learning at the wireless network edge, where limited power
wireless devices, each with its own dataset, build a joint model with the help of a remote …

We study federated machine learning (ML) at the wireless edge, where power-and
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …

We study federated edge learning (FEEL), where wireless edge devices, each with its own
dataset, learn a global model collaboratively with the help of a wireless access point acting …

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 …

This article investigates the analog gradient aggregation (AGA) solution to overcome the
communication bottleneck for wireless federated learning applications by exploiting the idea …

We study distributed machine learning at the wireless edge, where limited power devices
(workers) with local datasets implement distributed stochastic gradient descent (DSGD) over …

Federated learning (FL) is a popular distributed learning paradigm, in which a global model
at a server learns private data of clients without data shared among clients or the server. In …

In this paper, a sparsity promoting adaptive algorithm for distributed learning in diffusion
networks is developed. The algorithm follows the set-theoretic estimation rationale. At each …

In this paper, the problem of adaptive distributed learning in diffusion networks is
considered. The algorithms are developed within the convex set theoretic framework. More …