To leverage data and computation capabilities of mobile devices, machine learning
algorithms are deployed at the network edge for training artificial intelligence (AI) models …

To exploit massive amounts of data generated at mobile edge networks, federated learning
(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …

In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly
train a neural network by communicating learning updates with an access point without …

The marriage of wireless big data and machine learning techniques revolutionizes wireless
systems by introducing data-driven philosophy. However, the ever exploding data volume …

The deployment of federated learning in a wireless network, called federated edge learning
(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model …

The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed
as an enhancement of federated learning model. Such a system generally consists of three …

Federated learning (FL) is an emerging paradigm to train a global deep neural network
(DNN) model by collaborative clients that store their private data locally through the …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

There is a growing interest in the wireless communications community to complement the
traditional model-driven design approaches with data-driven machine learning (ML)-based …

Cooperative training methods for distributed machine learning typically assume noiseless
and ideal communication channels. This work studies some of the opportunities and …