Most existing wireless federated learning (FL) studies focused on homogeneous model
settings where devices train identical local models. In this setting, the devices with poor …

The data heterogeneity across clients and the limited communication resources, eg,
bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL) …

Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum),
deploying federated learning (FL) over wireless networks is challenged by frequent FL …

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 (FL) has recently become one of the hottest focuses in wireless edge
networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs …

In this article, we study the latency minimization problem for a wireless federated learning
(FL) system with heterogeneous computation capability, where different edge devices …

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

In this paper, we propose a communication-efficient federated learning (FL) framework to
enhance the convergence rate of FL under limited uplink capacity. The core idea of our …

In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …

Despite the recent advancements achieved by federated learning (FL), its real-world
deployment is significantly impeded by the heterogeneous learning environment …