Dispatching and Control Information Freshness-Aware Federated Learning for Simplified Power IoT-Reference-Cited by-同舟云学术

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Dispatching and Control Information Freshness-Aware Federated Learning for Simplified Power IoT

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:GLOBECOM 2022 - 2022 IEEE Global Communications Conference
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Author:

Jia Zehan¹,Yu Ziqi¹,Liao Haijun¹,Wang Zhao¹,Zhou Zhenyu¹,Wang Xiaoyan²,He Guoqing³,Mumtaz Shahid⁴,Guizani Mohsen⁵

Affiliation:

1. North China Electric Power University,Hebei Key Laboratory of Power Internet of Things Technology,Beijing, Baoding,Hebei,China,102206, 071003

2. Graduate School of Science and Engineering, Ibaraki University,Ibaraki-ken,Japan

3. China Electric Power Research Institute,State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems,Beijing,China,100192

4. Instituto de Telecomunicações,Aveiro,Portugal,3810-193

5. Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI),Machine Learning Department,UAE

Funder

Science and Technology Project of State Grid Corporation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10000063/10000593/10001183.pdf?arnumber=10001183

Reference21 articles.

1. Adaptive Transmission for Reconfigurable Intelligent Surface-Assisted OFDM Wireless Communications

2. Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing

3. Secure and Latency-Aware Digital Twin Assisted Resource Scheduling for 5G Edge Computing-Empowered Distribution Grids

4. QoE-aware efficient content distribution scheme for satellite- terrestrial networks;jiang;IEEE Trans Mobile Comput,2021

5. Wireless Access Control in Edge-Aided Disaster Response: A Deep Reinforcement Learning-Based Approach

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