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 …
Y Liao, Y Xu, H Xu, L Wang… - IEEE INFOCOM 2023-IEEE …, 2023 - ieeexplore.ieee.org
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 …
Z Jiang, Y Xu, H Xu, Z Wang, J Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
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 …
EO Sodiya, UJ Umoga, A Obaigbena, BS Jacks… - International Journal of …, 2024 - ijsra.net
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 …