In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …

Abstract In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train
Machine Learning (ML) models across the participants of a federation while preserving data …

Federated learning (FL) has emerged as a promising distributed learning paradigm that
enables a large number of mobile devices to cooperatively train a model without sharing …

The burgeoning growth of the Internet of Things (IoT) has prompted a paradigm shift in
computing architectures, leading to the emergence and rapid evolution of edge computing …

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user
privacy, federated learning (FL) is emerging as a promising technique to train a machine …

This systematic review paper delves into the evolving landscape of cloud architectures for
distributed edge computing, with a particular focus on decentralized and collaborative …

Traditional Federated Learning (FL) is a promising paradigm that enables massive edge
clients to collaboratively train deep neural network (DNN) models without exposing raw data …