Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges-Reference-Cited by-同舟云学术

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Published:2023-06 Issue:3 Volume:7 Page:957-979
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Al-Quraan Mohammad¹^ORCID,Mohjazi Lina¹^ORCID,Bariah Lina²^ORCID,Centeno Anthony¹^ORCID,Zoha Ahmed¹^ORCID,Arshad Kamran³^ORCID,Assaleh Khaled³^ORCID,Muhaidat Sami⁴^ORCID,Debbah Mérouane⁵,Imran Muhammad Ali¹^ORCID

Affiliation:

1. James Watt School of Engineering, University of Glasgow, Glasgow, U.K.

2. Technology Innovation Institute 9639 Masdar City, Abu Dhabi, UAE

3. Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman University, Ajman, UAE

4. Center for Cyber-Physical Systems, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE

5. Technology Innovation Institute, Abu Dhabi, UAE

Funder

Ajman University Internal Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computational Mathematics,Control and Optimization,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7433297/10132901/10081195.pdf?arnumber=10081195

Reference136 articles.

1. Federated Learning Empowered End-Edge-Cloud Cooperation for 5G HetNet Security

2. Centralized and Federated Learning for Predictive VNF Autoscaling in Multi-Domain 5G Networks and Beyond

3. Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO

4. Federated learning in mobile edge computing: An edge-learning perspective for beyond 5G;jere,2020

5. Deep Federated Q-Learning-Based Network Slicing for Industrial IoT

Cited by 32 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions;Future Generation Computer Systems;2024-11

2. Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution Using Radar-Aided Dynamic Blockage Recognition;IEEE Transactions on Mobile Computing;2024-10

3. A Survey of Federated Learning for mmWave Massive MIMO;IEEE Internet of Things Journal;2024-08-15

4. Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers;ACM Transactions on Intelligent Systems and Technology;2024-07-17

5. Researching the CNN Collaborative Inference Mechanism for Heterogeneous Edge Devices;Sensors;2024-06-27