Reliable federated learning for mobile networks
Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, for example, mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In federated learning, training d...
Main Authors: | Kang, Jiawen, Xiong, Zehui, Niyato, Dusit, Zou, Yuze, Zhang, Y., Guizani, M. |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/154439 |
Similar Items
-
A game-based incentive-driven offloading framework for dispersed computing
by: Wu, Hongjia, et al.
Published: (2023) -
Federated learning in mobile edge networks : a comprehensive survey
by: Lim, Bryan Wei Yang, et al.
Published: (2020) -
Hierarchical incentive mechanism design for federated machine learning in mobile networks
by: Lim, Bryan Wei Yang, et al.
Published: (2020) -
Deep reinforcement learning for mobile 5g and beyond : fundamentals, applications, and challenges
by: Xiong, Zehui, et al.
Published: (2020) -
Design of Y-shaped tri-band rectangular slot DGS patch antenna at sub-6 GHz frequency range for 5G communication
by: Ajay Singh, et al.
Published: (2024-07-01)