Federated learning in mobile edge networks : a comprehensive survey

Federated learning in mobile edge networks : a comprehensive survey

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-base...

Saved in:

書目詳細資料
Main Authors:	Lim, Bryan Wei Yang, Luong, Nguyen Cong, Hoang, Dinh Thai, Jiao, Yutao, Liang, Ying-Chang, Yang, Qiang, Niyato, Dusit, Miao, Chunyan
其他作者:	School of Computer Science and Engineering
格式:	Article
語言:	English
出版:	2020
主題:	Engineering::Computer science and engineering Federated Learning Mobile Edge Networks
在線閱讀:	https://hdl.handle.net/10356/144291
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

相似書籍

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)

Multi-agent deep reinforcement learning based incentive mechanism for multi-task federated edge learning
由: Zhao, Nan, et al.
出版: (2023)

Federated machine learning for edge computing
由: Hong, Zhi Hao
出版: (2025)

Applications of auction and mechanism design in edge computing: a survey
由: Qiu, Houming, et al.
出版: (2023)

Evaluating communication frameworks for federated deep learning in simulated edge environments
由: Ong, Aden Yew
出版: (2025)

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

When information freshness meets service latency in federated learning : a task-aware incentive scheme for smart industries
由: Lim, Bryan Wei Yang, et al.
出版: (2021)

Towards federated learning in UAV-enabled internet of vehicles : a multi-dimensional contract-matching approach
由: Lim, Bryan Wei Yang, et al.
出版: (2021)

Collate: collaborative neural network learning for latency-critical edge systems
由: Huai, Shuo, et al.
出版: (2023)

A novel joint dataset and incentive management mechanism for federated learning over MEC
由: Lee, Joohyung, et al.
出版: (2023)

Computation offloading and content caching and delivery in Vehicular Edge Network: a survey
由: Dziyauddin, Rudzidatul Akmam, et al.
出版: (2022)

Dynamics in coded edge computing for IoT: a fractional evolutionary game approach
由: Han, Yue, et al.
出版: (2023)

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

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

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

Securing federated learning: a covert communication-based approach
由: Xie, Yuan-Ai, et al.
出版: (2024)

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

When mobile blockchain meets edge computing
由: Xiong, Zehui, et al.
出版: (2020)

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)

Hybrid market-based resources allocation in Mobile Edge Computing systems under stochastic information
由: Huang, Xiaowen, et al.
出版: (2022)

Distributed algorithm for computation offloading in mobile edge computing considering user mobility and task randomness
由: Zheng, F. Yifeng, et al.
出版: (2022)

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

Selective autonomy or federalism: Options for the Phil.
由: Limpo, Jose Mari U., et al.
出版: (1990)

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

Blockchain-based privacy-preserving federated learning for mobile crowdsourcing
由: Ma, Haiying, et al.
出版: (2023)

Boosting privately: Federated extreme gradient boosting for mobile crowdsensing
由: LIU, Yang, et al.
出版: (2020)

Reimagining Federalism in India: Exploring the Frontiers of Collaborative Federal Architecture
由: Sharma, Chanchal Kumar
出版: (2015)

A non-economist's reply to the president's economic managers on federalism
由: Contreras, Antonio P.
出版: (2018)

Security modeling and efficient computation offloading for service workflow in mobile edge computing
由: Huang, Binbin, et al.
出版: (2020)

A survey on federated unlearning: challenges, methods, and future directions
由: Liu, Ziyao, et al.
出版: (2025)

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

Effective intrusion detection in heterogeneous Internet-of-Things networks via ensemble knowledge distillation-based federated learning
由: Shen, Jiyuan, et al.
出版: (2024)

The Origins of Holding-Together Federalism: Nepal, Myanmar, and Sri Lanka
由: Breen, Michael G.
出版: (2017)

Federalism: An alternation to the decentralization program of the government
由: Gonzales, Reynaldo, et al.
出版: (1989)

Edge intelligence for smart grid: a survey on application potentials
由: Gooi, Hoay Beng, et al.
出版: (2024)

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

Joint sponsored and edge caching content service market : a game-theoretic approach
由: Xiong, Zehui, et al.
出版: (2020)

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

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