Federated Learning (FL) is an emerging technique to enhance edge intelligence, where
mobile devices train machine learning models collaboratively with their local data. Limited …

Federated Learning has the potential to break the barrier of AI adoption at the edge through
better data privacy and reduced client to server communication cost. However, the …

In this paper, the problem of training federated learning (FL) algorithms over a wireless
network with mobile users is studied. In the considered model, several mobile users and a …

We consider the problem of convergence time minimization for federated learning (FL)
implemented in wireless systems. In such setups, each wireless edge device transmits its …

Federated learning (FL) can train a global model from clients' local data set, which can make
full use of the computing resources of clients and performs more extensive and efficient …

We consider a broadband over-the-air computation empowered model aggregation
approach for wireless federated learning (FL) systems and propose to leverage an …

Federated learning (FL) is a novel machine learning that performs distributed training locally
on devices and aggregating the local models into a global one. The limited network …

With the fast development of modern networking technologies, the transmission rate and
reliability of wireless networks have been greatly improved. Meanwhile, the fast-developing …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …