Clustered federated Multitask learning is introduced as an efficient technique when data is
unbalanced and distributed amongst clients in a non-independent and identically distributed …

In the rapidly evolving Internet of Things domain, managing voluminous data streams while
ensuring energy efficiency remains a cardinal challenge. Our research introduces a hybrid …

Federated learning (FL) has received significant attention in recent years for its advantages
in efficient training of machine learning models across distributed clients without disclosing …

Hierarchical Federated Learning (HFL) has emerged to overcome the shortcomings of
conventional Federated Learning (FL) due to communication obstacles between the end …

The constant surge in the traffic demand on cellular networks has led to continuous
expansion in network capacity in order to accommodate existing and new service demands …

To enhance the robustness and convergence of hierarchical federated learning (HFL) in
wireless networks with imperfect channel state information (CSI), a quantized HFL (QHFL) …

We propose a novel energy-efficient controlled descent algorithm (EECDA) for over-the-air
computation-assisted federated learning. In EECDA, the computing devices transmit their …

Next-generation mobile networks (NGMN) have ushered in unprecedented demands for
efficient data transfer and low-latency applications. Within the realm of 6G technology …

This paper focuses on the implementation of Pareto optimal front (POF) with deep neural
networks (DNNs) for optimizing and integrating of radio frequency (RF) designs. POF …