Federated Learning (FL) is a distributed machine learning paradigm that allows clients to
train models on their data while preserving their privacy. FL algorithms, such as Federated …

In this study, we present a novel approach for efficient resource allocation in a digital twin
(DT) framework for task offloading in a UAV-aided Internet-of-Vehicles (IoV) network. Our …

Federated Learning (FL) is a distributed machine learning approach that enables devices to
collaboratively train models without sharing their local data, ensuring user privacy and …

In order to deal with the problem of imbalance and complex feature relationship in network
data classification, this study proposes a machine learning classification method, combined …

Recently, decentralized learning has emerged as a popular peer-to-peer signal and
information processing paradigm that enables model training across geographically …

Most existing federated learning (FL) methodologies have assumed training begins from a
randomly initialized model. Recently, several studies have empirically demonstrated that …

Here, we introduce a framework leveraging a free-space optical system based on the 4f
configuration, inspired by the SWIFFT algorithms, designed to significantly enhance the …

This study introduces a combined machine learning strategy for optimizing resource
distribution within a digital twin (DT) setup, aimed at offloading tasks in UAV-supported …

Parameter-averaging, a method for combining multiple models into a single one, has
emerged as a promising approach to enhance performance without requiring additional …