Federated learning (FL) has been widely used to train machine learning models over
massive data in edge computing. However, the existing FL solutions may cause long …

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 …

We consider federated learning (FL) with multiple wireless edge servers having their own
local coverage. We focus on speeding up training in this increasingly practical setup. Our …

Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …

Federated learning (FL) is a framework for distributed learning of centralized models. In FL,
a set of edge devices train a model using their local data, while repeatedly exchanging their …

Federated learning (FL) has emerged in edge computing to address limited bandwidth and
privacy concerns of traditional cloud-based centralized training. However, the existing FL …

Federated edge learning (FEEL) is a promising distributed machine learning (ML)
framework to drive edge intelligence applications. However, due to the dynamic wireless …

Federated learning becomes a promising approach for preserving privacy by keeping user
data locally. The basic idea is that a central server iteratively aggregates distributed local …

Federated learning (FL) has recently emerged as a promising technology to enable artificial
intelligence (AI) at the network edge. To improve the communication efficiency of FL, over …