Z Zhao, J Wang, W Hong, TQS Quek… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Federated learning is a promising technique to implement network intelligence for the sixth generation (6G) communication systems. However, the collected data in wireless networks …
YJ Liu, S Qin, Y Sun, G Feng - IEEE Transactions on Wireless …, 2022 - ieeexplore.ieee.org
Federated learning (FL) has recently become one of the hottest focuses in wireless edge networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs …
Z Wang, Z Zhang, Y Tian, Q Yang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The conventional federated learning (FL) framework usually assumes synchronous reception and fusion of all the local models at the central aggregator and synchronous …
B Xu, W Xia, W Wen, P Liu, H Zhao… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Federated learning (FL) is promising in enabling large-scale model training by massive devices without exposing their local datasets. However, due to limited wireless resources …
X Han, J Li, W Chen, Z Mei, K Wei… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training. However, data …
There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user …
In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which …
Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum), deploying federated learning (FL) over wireless networks is challenged by frequent FL …
In this article, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In the considered model, wireless users execute an FL …