When information freshness meets service latency in federated learning : a task-aware incentive scheme for smart industries
For several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a Federated Learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries...
Main Authors: | Lim, Bryan Wei Yang, Xiong, Zehui, Kang, Jiawei, Niyato, Dusit, Leung, Cyril, Miao, Chunyan, Shen, Xuemin |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/152724 |
Similar Items
-
Decentralized edge intelligence : a dynamic resource allocation framework for hierarchical federated learning
by: Lim, Bryan Wei Yang, et al.
Published: (2022) -
Towards federated learning in UAV-enabled internet of vehicles : a multi-dimensional contract-matching approach
by: Lim, Bryan Wei Yang, et al.
Published: (2021) -
Dynamic edge association and resource allocation in self-organizing hierarchical federated learning networks
by: Lim, Bryan Wei Yang, et al.
Published: (2022) -
Hierarchical incentive mechanism design for federated machine learning in mobile networks
by: Lim, Bryan Wei Yang, et al.
Published: (2020) -
A novel joint dataset and incentive management mechanism for federated learning over MEC
by: Lee, Joohyung, et al.
Published: (2023)