Pushing artificial intelligence (AI) from central cloud to network edge has reached board
consensus in both industry and academia for materializing the vision of artificial intelligence …

As data generation increasingly takes place on devices without a wired connection, Machine
Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have …

In the 1940s, Claude Shannon developed the information theory focusing on quantifying the
maximum data rate that can be supported by a communication channel. Guided by this …

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 …

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 sixth generation (6G) wireless communication network presents itself as a promising
technique that can be utilized to provide a fully data-driven network evaluating and …

The ever-growing popularity and rapid development of artificial intelligence (AI) have raised
rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

Federated learning is a privacy-preserving approach to train a global model at a central
server by collaborating with wireless devices, each with its own local training data set. In this …

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