The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-
efficient adaptive federated learning at the wireless network edge, with latency and learning …

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-
efficient federated learning at the wireless network edge, with latency and learning …

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 …

Federated learning has gained popularity as a means of training models distributed across
the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) …

Federated Learning (FL) has been widely used to train shared machine learning models
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …

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

Federated learning provides a promising paradigm to enable network edge intelligence in
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …

The practical deployment of federated learning (FL) over wireless networks requires
balancing energy efficiency, convergence rate, and a target accuracy due to the limited …

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 …

The performance capabilities of models trained in a federated learning (FL) setting over
wireless networks can be significantly affected by the underlying properties of the …