In this paper, a communication-efficient federated learning (FL) framework is proposed for
improving the convergence rate of FL under a limited uplink capacity. The central idea of the …

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 …

Over-the-air federated learning (FL) is a recent paradigm to address the communication
bottleneck of FL, where a machine learning model is trained by aggregating the local …

Federated learning becomes increasingly attractive in the areas of wireless communications
and machine learning due to its powerful learning ability and potential applications. In …

This paper investigates efficient distributed training of a Federated Learning (FL) model over
a wireless network of wireless devices. The communication iterations of the distributed …

We study federated learning (FL), where power-limited wireless devices utilize their local
datasets to collaboratively train a global model with the help of a remote parameter server …

How to tackle the unreliability in wireless channels is critical for federated learning (FL). To
solve this problem, we propose a novel FL framework, namely FL with gradient recycling (FL …

Federated Learning (FL) is a distributed machine learning technique designed to utilize the
distributed datasets collected by our mobile and internet-of-things devices. As such, it is …

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data
distributions, computing powers, and channel conditions of participating devices. This paper …

In this article, we study device selection and resource allocation (DSRA) for layerwise
federated learning (FL) in wireless networks. For effective learning, DSRA should be …