This paper discusses nonparametric distributed learning. After reviewing the classical
learning model and highlighting the success of machine learning in centralized settings, the …

In this paper, we consider decentralized federated learning (FL) over wireless networks,
where over-the-air computation (AirComp) is adopted to facilitate the local model consensus …

The ongoing deployments of the Internet of Things (IoT)-based smart applications are
spurring the adoption of machine learning as a key technology enabler. To overcome the …

This chapter deals with decentralized learning algorithms for in-network processing of graph-
valued data. A generic learning problem is formulated and recast into a separable form …

A very large number of communications are typically required to solve distributed learning
tasks, and this critically limits scalability and convergence speed in wireless communications …

Machine learning (ML) is a promising enabler for the fifth generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

This article studies decentralized federated learning algorithms in wireless IoT networks.
The traditional parameter server architecture for federated learning faces some problems …

Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …

Conventional machine learning techniques are conducted in a centralized manner.
Recently, the massive volume of generated wireless data, the privacy concerns and the …

With the rise of machine learning (ML) and the proliferation of smart mobile devices, recent
years have witnessed a surge of interest in performing ML in wireless edge networks. In this …