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 …

Federated learning (FL) allows model training from local data by edge devices while
preserving data privacy. However, the learning accuracy decreases due to the heterogeneity …

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 …

Model update compression is a widely used technique to alleviate the communication cost
in federated learning (FL). However, there is evidence indicating that the compression …

For wireless federated learning (FL), this work proposes an adaptive model pruning-based
FL (AMP-FL) frame-work, where the edge server dynamically generates sub-models by …

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 …

Federated learning (FL) enables multiple clients to collaborate on a common learning task
via only exchanging model updates. With the progressive improvements in deep learning …

Federated learning (FL) has recently emerged as an attractive decentralized solution for
wireless networks to collaboratively train a shared model while keeping data localized. As a …

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 …