This paper considers improving wireless communication and computation efficiency in
federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge …

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 been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …

Communication has been known to be one of the primary bottlenecks of federated learning
(FL), and yet existing studies have not addressed the efficient communication design …

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 …

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 …

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 …

Communication is widely known as the primary bottleneck of federated learning, and
quantization of local model updates before uploading to the parameter server is an effective …

Recently, federated learning (FL) has sparked widespread attention as a promising
decentralized machine learning approach which provides privacy and low delay. However …

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 …