Federated Learning is a machine learning setting where the goal is to train a highquality
centralized model while training data remains distributed over a large number of clients …

With the ever improving computing capabilities and storage capacities of mobile devices in
line with evolving telecommunication network paradigms, there has been an explosion of …

Distributed training performs data-parallel training of DNN models which is a necessity for
increasingly complex models and large datasets. Recent works are identifying major …

Federated learning (FL) is a key enabler for efficient communication and computing,
leveraging devices' distributed computing capabilities. However, applying FL in practice is …

Adept network management is key for supporting extremely heterogeneous applications
with stringent quality of service (QoS) requirements; this is more so when envisioning the …

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 …

Federated learning is a very promising machine learning paradigm where a large number of
clients cooperatively train a global model using their respective local data. In this paper, we …

We consider federated learning in a wireless edge network, where multiple power-limited
mobile devices collaboratively train a global model, using their local data with the assistance …

Unlike theoretical analysis of distributed learning (DL) in the literature, DL over wireless
edge networks faces the inherent dynamics/uncertainty of wireless connections and edge …

The exploding increase in the number of connected devices and growing sizes of their
generated data gave more opportunities for distributed learning to dominate fast data …