Quality-aware Client Selection and Resource Optimization for Federated Learning in Computing Networks-Reference-Cited by-同舟云学术

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Quality-aware Client Selection and Resource Optimization for Federated Learning in Computing Networks

Published:2024-06-09 Issue: Volume: Page:2628-2633
ISSN:
Container-title:ICC 2024 - IEEE International Conference on Communications
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Author:

Liao Yanyan¹,Feng Jie¹,Zhou Zongjie¹,Shang Bodong²,Liu Lei³,Pei Qingqi¹

Affiliation:

1. State Key Laboratory of Integrated Service Networks, Xidian University,Xi'an,China,710071

2. Eastern Institute for Advanced Study, Eastern Institute of Technology,Ningbo,China,315200

3. Guangzhou Institute of Technology, Xidian University,Guangzhou,China,510555

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10622104/10622158/10622738.pdf?arnumber=10622738

Reference12 articles.

1. Efficient Federated Learning in Resource-Constrained Edge Intelligence Networks using Model Compression

2. FedGraph: Federated Graph Learning With Intelligent Sampling

3. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

4. Device Scheduling with Fast Convergence for Wireless Federated Learning

5. Joint Client Scheduling and Resource Allocation Under Channel Uncertainty in Federated Learning

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