Federated learning (FL) enables decentralized model training, but the distribution of data
across devices presents significant challenges to global model convergence. Existing …

Cyberbullying has emerged as a pervasive issue in the digital age, necessitating advanced
techniques for effective detection and mitigation. This research explores the integration of …

Network management is becoming more complex due to various factors. The growth of IoT
increases the number of nodes to control. The combination of Edge and Fog Computing with …

Abstract Decentralized Federated Learning (DFL) enables collaborative model training
across multiple devices without relying on a central server, thus preserving data privacy and …

The emergence of the Cognitive Internet of Medical Things (CIoMT) during the COVID-19
pandemic has been transformational. The CIoMT is a rapidly evolving technology that uses …

A distributed machine learning technique called federated learning allows numerous
Internet of Things (IoT) edge devices to work together to train a model without sharing their …

Recently, a noticeable increase in the shortage of energy resources has been observed,
coupled with a rapidly escalating demand for energy. In response to this challenge, this …

The rapid growth of the Federated Internet of Things ecosystem has introduced new
challenges in achieving seamless connectivity and interoperability across heterogeneous …

The upcoming 6G networks aim for fully automated, intelligent network functionalities and
services. Therefore, ML is essential for these networks. Given stringent privacy regulations …

Decentralised federated learning (DFL) transforms collaborative energy consumption
prediction using distributed computation across a large network of edge nodes, ensuring …