Heterogeneous federated learning: State-of-the-art and research challenges

M Ye, X Fang, B Du, PC Yuen, D Tao - ACM Computing Surveys, 2023 - dl.acm.org
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated learning as a privacy solution-an overview

M Khan, FG Glavin, M Nickles - Procedia Computer Science, 2023 - Elsevier
Abstract The Fourth Industrial Revolution suggests smart and automated industrial solutions
by incorporating Artificial Intelligence into it. Today, the world of technology is highly …

Stochastic client selection for federated learning with volatile clients

T Huang, W Lin, L Shen, K Li… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Federated learning (FL), arising as a privacy-preserving machine learning paradigm, has
received notable attention from the public. In each round of synchronous FL training, only a …

Online client selection for asynchronous federated learning with fairness consideration

H Zhu, Y Zhou, H Qian, Y Shi, X Chen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Federated learning (FL) leverages the private data and computing power of multiple clients
to collaboratively train a global model. Many existing FL algorithms over wireless networks …

Wireless distributed learning: A new hybrid split and federated learning approach

X Liu, Y Deng, T Mahmoodi - IEEE Transactions on Wireless …, 2022 - ieeexplore.ieee.org
Cellular-connected unmanned aerial vehicle (UAV) with flexible deployment is foreseen to
be a major part of the sixth generation (6G) networks. The UAVs connected to the base …

Adaptive hierarchical federated learning over wireless networks

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 …

Exploring deep-reinforcement-learning-assisted federated learning for online resource allocation in privacy-preserving edgeiot

J Zheng, K Li, N Mhaisen, W Ni… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
Federated learning (FL) has been increasingly considered to preserve data training privacy
from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT) …

Federated learning-empowered mobile network management for 5G and beyond networks: From access to core

J Lee, F Solat, TY Kim, HV Poor - … Communications Surveys & …, 2024 - ieeexplore.ieee.org
The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …

Federated Learning-based Misbehaviour detection on an emergency message dissemination scenario for the 6G-enabled Internet of Vehicles

LJ Vinita, V Vetriselvi - Ad Hoc Networks, 2023 - Elsevier
With the 6G-enabled Internet of Vehicles (IoV), the Intelligent Transportation System (ITS)
uses new communication technologies and smart data analysis to make transportation …

Reputation-aware hedonic coalition formation for efficient serverless hierarchical federated learning

JS Ng, WYB Lim, Z Xiong, X Cao, J Jin… - … on Parallel and …, 2021 - ieeexplore.ieee.org
Amid growing concerns on data privacy, Federated Learning (FL) has emerged as a
promising privacy preserving distributed machine learning paradigm. Given that the FL …