Communication efficiency is of importance for wireless federated learning systems. In this
paper, we propose a communication-efficient strategy for federated learning over multiple …

The communication bottleneck of over-the-air federated learning (OA-FL) lies in uploading
the gradients of local learning models. In this paper, we study the reduction of the …

We provide the optimal receive combining strategy for federated learning in multiple-input
multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform …

In federated learning (FL), the communication constraint between the remote clients and the
Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be …

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 …

Federated learning is a privacy-preserving approach to train a global model at a central
server by collaborating with wireless devices, each with its own local training data set. In this …

In this paper, we present a communication-efficient federated learning framework inspired
by quantized compressed sensing. The presented framework consists of gradient …

This paper aims to integrate two synergetic technologies, federated learning (FL) and width-
adjustable slimmable neural network (SNN) architectures. FL preserves data privacy by …

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 study federated machine learning at the wireless network edge, where limited power
wireless devices, each with its own dataset, build a joint model with the help of a remote …