Federated learning for smart cities: A comprehensive survey

S Pandya, G Srivastava, R Jhaveri, MR Babu… - Sustainable Energy …, 2023 - Elsevier
With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big
data, fog computing, and edge computing, smart city applications have suffered from issues …

Green edge AI: A contemporary survey

Y Mao, X Yu, K Huang, YJA Zhang… - Proceedings of the …, 2024 - ieeexplore.ieee.org
Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …

Two-layer federated learning with heterogeneous model aggregation for 6g supported internet of vehicles

X Zhou, W Liang, J She, Z Yan, I Kevin… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-
dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to …

Clustered federated learning in internet of things: Convergence analysis and resource optimization

B Xu, W **a, H Zhao, Y Zhu, X Sun… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Federated learning (FL) framework enables user devices to collaboratively train a global
model based on their local data sets without privacy leak. However, the training performance …

One bit aggregation for federated edge learning with reconfigurable intelligent surface: Analysis and optimization

H Li, R Wang, W Zhang, J Wu - IEEE Transactions on Wireless …, 2022 - ieeexplore.ieee.org
As one of the most popular and attractive frameworks for model training, federated edge
learning (FEEL) presents a new paradigm, which avoids direct data transmission by …