Enhancing federated learning with spectrum allocation optimization and device selection

Enhancing federated learning with spectrum allocation optimization and device selection

Machine learning (ML) is a widely accepted means for supporting customized services for mobile devices and applications. Federated Learning (FL), which is a promising approach to implement machine learning while addressing data privacy concerns, typically involves a large number of wireless mobile d...

Saved in:

書目詳細資料
Main Authors:	Zhang, Tinghao, Lam, Kwok-Yan, Zhao, Jun, Li, Feng, Han, Huimei, Norziana Jamil
其他作者:	School of Computer Science and Engineering
格式:	Article
語言:	English
出版:	2023
主題:	Engineering::Electrical and electronic engineering::Wireless communication systems Federated Learning Spectrum Allocation Optimization
在線閱讀:	https://hdl.handle.net/10356/168030
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Nanyang Technological University
語言:	English

相似書籍

Joint optimization of energy consumption and completion time in federated learning
由: Zhou, Xinyu, et al.
出版: (2022)

Spectrum allocation with asymmetric monopoly model for multibeam-based cognitive satellite networks
由: Li, Feng, et al.
出版: (2018)

Deep reinforcement learning based scheduling strategy for federated learning in sensor-cloud systems
由: Zhang, Tinghao, et al.
出版: (2023)

Joint device scheduling and bandwidth allocation for federated learning over wireless networks
由: Zhang, Tinghao, et al.
出版: (2024)

Dynamic spectrum access for Internet-of-Things with hierarchical federated deep reinforcement learning
由: Zhang, Songbo, et al.
出版: (2023)

Spectrum optimization for cognitive satellite communications with Cournot game model
由: Wang, Li, et al.
出版: (2018)

Spectrum pricing for cognitive radio networks with user’s stochastic distribution
由: Wang, Li, et al.
出版: (2020)

Design and analysis of optimal resource allocation policies in wireless networks
由: WANG BANG
出版: (2010)

Federated learning for green shipping optimization and management
由: Wang, Haoqing, et al.
出版: (2023)

Dynamic edge association and resource allocation in self-organizing hierarchical federated learning networks
由: Lim, Bryan Wei Yang, et al.
出版: (2022)

Decentralized edge intelligence : a dynamic resource allocation framework for hierarchical federated learning
由: Lim, Bryan Wei Yang, et al.
出版: (2022)

Fairness-aware reverse auction-based federated learning
由: Tang, Xiaoli, et al.
出版: (2025)

Distributed decisions on TV spectrum allocation considering spatial and temporal variation
由: Chen, Zhenwei, et al.
出版: (2018)

Spectrum trading for satellite communication systems with dynamic bargaining
由: Li, Feng, et al.
出版: (2019)

Joint client-and-sample selection for federated learning via bi-level optimization
由: Li, Anran, et al.
出版: (2024)

Privacy and robustness in federated learning: attacks and defenses
由: Lyu, Lingjuan, et al.
出版: (2023)

Membership inference vulnerabilities in peer-to-peer federated learning
由: Luqman, Alka, et al.
出版: (2024)

CrowdFL: a marketplace for crowdsourced federated learning
由: Feng, Daifei, et al.
出版: (2022)

Blockchain-enabled federated learning with mechanism design
由: Toyoda, Kentaroh, et al.
出版: (2021)

Spectrum optimization in cognitive satellite networks with graph coloring method
由: Wang, Li, et al.
出版: (2020)

MarS-FL: enabling competitors to collaborate in federated learning
由: Wu, Xiaohu, et al.
出版: (2023)

Towards efficient and certified recovery from poisoning attacks in federated learning
由: Jiang, Yu, et al.
出版: (2025)

Peer-to-peer federated learning
由: Sim, Nicholas Yong Yue
出版: (2024)

Semi-supervised federated heterogeneous transfer learning
由: Feng, Siwei, et al.
出版: (2022)

Trading-based dynamic spectrum access and allocation in cognitive internet of things
由: Li, Feng, et al.
出版: (2020)

Device scheduling and bandwidth allocation for federated learning over wireless networks
由: Zhang, Tinghao, et al.
出版: (2024)

Hybrid Federalization in India, Sri Lanka and Nepal
由: Allison-Reumann, Laura, et al.
出版: (2018)

A blockchain based federated learning for message dissemination in vehicular networks
由: Ayaz, Ferheen, et al.
出版: (2022)

Content popularity prediction based on quantized federated Bayesian learning in fog radio access networks
由: Tao, Yunwei, et al.
出版: (2023)

Device scheduling and assignment in hierarchical federated learning for Internet of Thing
由: Zhang, Tinghao, et al.
出版: (2024)

Deep anomaly detection for time-series data in industrial IoT: a communication-efficient on-device federated learning approach
由: Liu, Yi, et al.
出版: (2022)

Covert federated learning via intelligent reflecting surfaces
由: Zheng, Jie, et al.
出版: (2023)

Robust semi-supervised federated learning for images automatic recognition in internet of drones
由: Zhang, Zhe, et al.
出版: (2023)

Dynamic spectrum access networks with heterogeneous users : how to price the spectrum?
由: Li, Feng, et al.
出版: (2019)

Distribution-balanced federated learning for fault identification of power lines
由: Wang, Tianjing, et al.
出版: (2023)

Privacy-preserving asynchronous federated learning under non-IID settings
由: MIAO, Yinbin, et al.
出版: (2024)

EasyFL: a low-code federated learning platform for dummies
由: Zhuang, Weiming, et al.
出版: (2023)

Enhancing dynamic security assessment in smart grids through quantum federated learning
由: Ren, Chao, et al.
出版: (2025)

FeSA: Automatic federated swarm attestation on dynamic large-scale IoT devices
由: KUANG, Boyu, et al.
出版: (2023)

Federal systems: A comparison of the federal Republic of Germany and the proposed federal Republic of the Philippines
由: Alano, Don Jon
出版: (2004)