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 …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

Communication on heterogeneous edge networks is a fundamental bottleneck in Federated
Learning (FL), restricting both model capacity and user participation. To address this issue …

Abstract Cross-device Federated Learning (FL) is a distributed learning paradigm with
several challenges that differentiate it from traditional distributed learning: variability in the …

Federated learning is a communication-efficient training process that alternate between
local training at the edge devices and averaging of the updated local model at the center …

Federated learning enables training a global model from data located at the client nodes,
without data sharing and moving client data to a centralized server. Performance of …

In this paper, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In particular, in the considered model, wireless users perform an …

Federated Learning is an emerging direction in distributed machine learning that en-ables
jointly training a model without sharing the data. Since the data is distributed across many …

Federated learning is a promising technique to implement network intelligence for the sixth
generation (6G) communication systems. However, the collected data in wireless networks …

Federated Edge Learning (FEEL) involves the collaborative training of machine learning
models among edge devices, with the orchestration of a server in a wireless edge network …