Efficient Wireless Network Slicing in 5G Networks: An Asynchronous Federated Learning Approach
Author:
Affiliation:
1. Lakehead University,Department of Computer Science,Thunder Bay,Ontario,Canada
2. Idaho State University,Department of Electrical and Computer Engineering,Pocatello,ID,USA
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9975794/9975848/09976007.pdf?arnumber=9976007
Reference18 articles.
1. On Predicting Service-oriented Network Slices Performances in 5G: A Federated Learning Approach
2. Statistical Federated Learning for Beyond 5G SLA-Constrained RAN Slicing
3. Joint Selection of Local Trainers and Resource Allocation for Federated Learning in Open RAN Intelligent Controllers
4. Convolutional Neural Networks for patient-specific ECG classification
5. A Deep Learning Method for Predictive Channel Assignment in Beyond 5G Networks
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