The conventional federated learning (FL) framework usually assumes synchronous
reception and fusion of all the local models at the central aggregator and synchronous …

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 promising technique to implement network intelligence for the sixth
generation (6G) communication systems. However, the collected data in wireless networks …

Federated learning is a communication-efficient training process that alternate between
local training at the edge devices and averaging of the updated local model at the center …

Federated learning (FL) has recently emerged as an important and promising learning
scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets …

In this paper, the problem of delay minimization for federated learning (FL) over wireless
communication networks is investigated. In the considered model, each user exploits limited …

In this paper, the convergence time of federated learning (FL), when deployed over a
realistic wireless network, is studied. In particular, with the considered model, wireless users …

The newly emerging federated learning (FL) framework offers a new way to train machine
learning models in a privacy-preserving manner. However, traditional FL algorithms are …

Communication limitation at the edge is widely recognized as a major bottleneck for
federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to …

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 …