Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

In this paper, we study a relay-assisted federated edge learning (FEEL) network under
latency and bandwidth constraints. In this network, users collaboratively train a global model …

Training a machine learning model with federated edge learning (FEEL) is typically time
consuming due to the constrained computation power of edge devices and the limited …

This paper studies the joint device selection and power control scheme for wireless
federated learning (FL), considering both the downlink and uplink communications between …

As a promising distributed learning paradigm, federated learning (FL) involves training deep
neural network (DNN) models at the network edge while protecting the privacy of the edge …

Batch Normalization (BN) is commonly used in modern deep neural networks (DNNs) to
improve stability and speed up convergence during centralized training. In federated …

Federated learning is a popular distributed machine learning paradigm that enables
collaborative model training at multiple entities via exchanging intermediate learning results …

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-
efficient adaptive federated learning at the wireless network edge, with latency and learning …

Federated Learning is a modern decentralized machine learning technique where user
equipments perform machine learning tasks locally and then upload the model parameters …

Federated Learning (FL) is a promising privacy-preserving distributed learning paradigm but
suffers from high communication cost when training large-scale machine learning models …