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 an efficient and privacy-preserving distributed learning paradigm
that enables massive edge devices to train machine learning models collaboratively …

Federated Learning (FL) is a distributed learning framework that enables collaborative
model training without raw data sharing. However, due to the shortage of memory resources …

The conventional federated learning (FL) framework usually assumes synchronous
reception and fusion of all the local models at the central aggregator and synchronous …

In this paper, the convergence time of federated learning (FL), when deployed over a
realistic wireless network, is studied. In particular, with the considered model, wireless users …

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 …

Federated learning (FL) over resource-constrained wireless networks has recently attracted
much attention. However, most existing studies consider one FL task in single-cell wireless …

Federated learning (FL) is an emerging distributed learning paradigm that collaboratively
trains a shared model while preserving their data privacy. However, clients participating in …

In this paper, the problem of training federated learning (FL) algorithms over a wireless
network with mobile users is studied. In the considered model, several mobile users and a …

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 …