Edge machine learning involves the deployment of learning algorithms at the wireless
network edge so as to leverage massive mobile data for enabling intelligent applications …

We study federated machine learning (ML) at the wireless edge, where power-and
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …

Federated edge learning (FEEL) is a popular framework for model training at an edge server
using data distributed at edge devices (eg, smart-phones and sensors) without …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Federated learning (FL) enables geographically dispersed edge devices (ie, clients) to learn
a global model without sharing the local datasets, where each client performs gradient …

We consider a many-to-one wireless architecture for federated learning at the network edge,
where multiple edge devices collaboratively train a model using local data. The unreliable …

Training task in classical machine learning models, such as deep neural networks, is
generally implemented at a remote cloud center for centralized learning, which is typically …

Training a machine learning model with federated edge learning (FEEL) is typically time
consuming due to the constrained computation power of edge devices and the limited …

This paper focuses on communication-efficient federated learning problem, and develops a
novel distributed quantized gradient approach, which is characterized by adaptive …

Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …