Decentralized edge intelligence : a dynamic resource allocation framework for hierarchical federated learning

Decentralized edge intelligence : a dynamic resource allocation framework for hierarchical federated learning

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is pro...

Saved in:

書目詳細資料
Main Authors:	Lim, Bryan Wei Yang, Ng, Jer Shyuan, Xiong, Zehui, Jin, Jiangming, Zhang, Yang, Niyato, Dusit, Leung, Cyril, Miao, Chunyan
其他作者:	School of Computer Science and Engineering
格式:	Article
語言:	English
出版:	2022
主題:	Engineering::Computer science and engineering Federated Learning Edge Intelligence
在線閱讀:	https://hdl.handle.net/10356/156035
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)

Federated learning in mobile edge networks : a comprehensive survey
由: Lim, Bryan Wei Yang, et al.
出版: (2020)

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)

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)

A double auction mechanism for resource allocation in coded vehicular edge computing
由: Ng, Jer Shyuan, et al.
出版: (2022)

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)

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

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

Hierarchical incentive mechanism design for federated machine learning in mobile networks
由: Lim, Bryan Wei Yang, et al.
出版: (2020)

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

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

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

Collate: collaborative neural network learning for latency-critical edge systems
由: Huai, Shuo, 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)

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)

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

Auditable and verifiable federated learning based on blockchain-enabled decentralization
由: Kalapaaking, Aditya Pribadi, et al.
出版: (2024)

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

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)

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

Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles
由: Ng, Jer Shyuan, et al.
出版: (2021)

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

Towards fairness-aware federated learning
由: Shi, Yuxin, et al.
出版: (2024)

Resource allocation for federated learning enabled edge intelligence
由: Lim, Bryan Wei Yang
出版: (2022)

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

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

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

Efficient edge detection using hierarchical structures
由: Tan, C.L., et al.
出版: (2014)

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

Human rights violations in Chechnya and the Russian Federation: Effects and resolution (1990s present)
由: Africa, Ma. Aira Angelica M., et al.
出版: (2007)

Enhancing federated learning with spectrum allocation optimization and device selection
由: Zhang, Tinghao, et al.
出版: (2023)

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)

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

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

Byzantine-resilient decentralized stochastic gradient descent
由: Guo, Shangwei, et al.
出版: (2024)

Federated learning-powered visual object detection for safety monitoring
由: Liu, Yang, et al.
出版: (2024)

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