Toward Ambient Intelligence: Federated Edge Learning With Task-Oriented Sensing, Computation, and Communication Integration-Reference-Cited by-同舟云学术

Toward Ambient Intelligence: Federated Edge Learning With Task-Oriented Sensing, Computation, and Communication Integration

Published:2023-01 Issue:1 Volume:17 Page:158-172
ISSN:1932-4553
Container-title:IEEE Journal of Selected Topics in Signal Processing
language:
Short-container-title:IEEE J. Sel. Top. Signal Process.

Author:

Liu Peixi¹^ORCID,Zhu Guangxu²^ORCID,Wang Shuai³^ORCID,Jiang Wei¹^ORCID,Luo Wu¹,Poor H. Vincent⁴^ORCID,Cui Shuguang²^ORCID

Affiliation:

1. State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, Peking University, Beijing, China

2. Shenzhen Research Institute of Big Data, Shenzhen, China

3. Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

4. Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA

Funder

National Key R&D Program of China

Basic Research Project

Hetao Shenzhen-HK S&T Cooperation Zone

National Natural Science Foundation of China

Guangdong Provincial Key Laboratory of Future Networks of Intelligence

Basic and Applied Basic Research Foundation of Guangdong Province

Shenzhen Key Laboratory of Big Data and Artificial Intelligence

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/4200690/10050192/9970330-aam.pdf

Reference39 articles.

1. A joint learning and communications framework for federated learning over wireless networks;chen;IEEE Trans Wireless Commun,2021

2. Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD;wang;Proc 2nd Conf Mach Learn Syst (MLSys),0

3. Federated Learning Over Wireless Fading Channels

4. Energy Efficient Federated Learning Over Wireless Communication Networks

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