… causes performance issues in contemporary fog networking environments, however. Next,
we outline the key considerations for developing network-aware techniques for distributing …

… Abstract— 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 …

Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

… (5G) and beyond cellular networks, in which wireless devices in … through cellular infrastructures
such as base stations (BSs). … the distributed ML-model training in a mobile edge learning …

… , and distributed Machine Learning (ML) with its potential to avoid the transmission of the
large dataset and possible compromise of privacy that may exist in cloud-based centralized …

… of distributed training of a machine learning model over the nodes of a wireless communication
network. Existing … We conclude that a codesign of distributed optimization algorithms and …

… popular machine learning techniques utilized in wireless networks, … The applications of
machine learning in resource management … According to [68], distributed reinforcement learning …

… machine learning at the edge of wireless networks (such as edge cloud), close to mobile users,
is critical for future wireless networks… from existing machine learning configurations in the …

… In this paper, we investigate distributed machine learning for a multiuser mobile edge …
sharing for distributed machine learning at wireless network edge,” IEEE Wireless Commun. …

… The goal of this article is to provide a holistic overview of relevant communication and machine
learning (ML) principles, and thereby present communication-efficient and distributed ML …