The evolution of wireless networks, from first-generation (1G) to fifth generation (5G), has
facilitated real-time services and intelligent applications powered by artificial intelligence (AI) …

This work aims to address two of the main challenges for federated learning (FL), ie, the
limited communication resources and the data heterogeneity across devices. To this end, we …

Progressing towards a new era of Artificial Intelligence (AI)-enabled wireless networks,
concerns regarding the environmental impact of AI have been raised both in industry and …

After researchers devoted considerable efforts to developing 5G standards, their passion
began to focus on establishing the basics for the standardization of 6G and beyond. The …

Federated Learning (FL), as an effective decentral-ized approach, has attracted
considerable attention in privacy-preserving applications for wireless edge networks. In …

The practical deployment of federated learning (FL) over wireless networks requires
balancing energy efficiency, convergence rate, and a target accuracy due to the limited …

Fiber-wireless access technology is an active research topic for future generation
communication networks. However, the nonlinear distortion of fiber can cause problems for …

The conventional model aggregation-based federated learning (FL) approach requires all
local models to have the same architecture, which fails to support practical scenarios with …

Today's advancement in Artificial Intelligence (AI) enables training Machine Learning (ML)
models on the daily-produced data by connected edge devices. To make the most of the …

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 …