To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

With growth in the number of smart devices and advancements in their hardware, in recent
years, data-driven machine learning techniques have drawn significant attention. However …

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 …

Edge machine learning involves the deployment of learning algorithms at the network edge
to leverage massive distributed data and computation resources to train artificial intelligence …

Federated Learning marks a turning point in the implementation of decentralized machine
learning (especially deep learning) for wireless devices by protecting users' privacy and …

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

Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

Federated learning has emerged recently as a promising solution for distributing machine
learning tasks through modern networks of mobile devices. Recent studies have obtained …

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

Federated edge learning (FEEL) is a popular distributed learning framework that allows
privacy-preserving collaborative model training via periodic learning-updates …