Distributed machine learning has been extensively employed in wireless systems, which
can leverage abundant data distributed over massive devices to collaboratively train a high …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

Decentralized learning enables edge users to collaboratively train models by exchanging
information via device-to-device communication, yet prior works have been limited to …

Mobile edge learning is an emerging technique that enables distributed edge devices to
collaborate in training shared machine learning (ML) models by exploiting their local data …

This paper considers a federated edge learning (FEL) system where a base station (BS)
coordinates a set of edge devices to train a shared machine learning model collaboratively …

Facing the heavy traffic burden and data privacy, distributed machine learning (DML) has
been envisioned as a promising computing paradigm to enable edge intelligence by …

Decentralized federated learning, inherited from decentralized learning, enables the edge
devices to collaborate on model training in a peer-to-peer manner without the assistance of …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing. To fully utilize the widely distributed data, we concentrate on a wireless …

Device-to-device (D2D)-assisted decentralized learning has been proposed for mobile
devices to collaboratively train artificial intelligence networks without the centralized …

Federated learning has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …