X Sha, W Sun, X Liu, Y Luo, C Luo - Electronics, 2024 - mdpi.com
Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number …
E Malan, V Peluso, A Calimera, E Macii… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Federated learning (FL) is a collaborative machine learning paradigm where network-edge clients train a global model under the orchestration of a central server. Unlike traditional …
L Wang, Y Xu, H Xu, Z Jiang, M Chen… - IEEE/ACM …, 2023 - ieeexplore.ieee.org
At the network edge, federated learning (FL) has gained attention as a promising approach for training deep learning (DL) models collaboratively across a large number of devices …
Y Jia, X Zhang, A Beheshti, W Dou - … of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
Federated Learning (FL) has emerged as a promising solution in Edge Computing (EC) environments to process the proliferation of data generated by edge devices. By …
Y Deng, F Lyu, T Xia, Y Zhou, Y Zhang… - IEEE/ACM …, 2024 - ieeexplore.ieee.org
Federated learning (FL) enables collaborative model training over distributed computing nodes without sharing their privacy-sensitive raw data. However, in FL, iterative exchanges …
Y Zhou, X Pang, Z Wang, J Hu, P Sun… - IEEE INFOCOM 2024 …, 2024 - ieeexplore.ieee.org
Federated learning (FL) is widely used in edge environments as a privacy-preserving collaborative learning paradigm. However, edge devices often have heterogeneous …
S Ye, L Zeng, Q Wu, K Luo, Q Fang… - Proceedings of the 51st …, 2022 - dl.acm.org
Federated Learning (FL) has been a promising paradigm in distributed machine learning that enables in-situ model training and global model aggregation. While it can well preserve …
Federated learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. However, despite its emerging applications in many areas …
Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) …