A Graph Neural Network Learning Approach to Optimize RIS-Assisted Federated Learning
Author:
Affiliation:
1. School of Information Science and Technology, ShanghaiTech University, Shanghai, China
2. Centre for Wireless Communication, University of Oulu, Oulu, Finland
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shanghai
Shanghai Rising-Star Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications
Link
http://xplorestaging.ieee.org/ielx7/7693/10247109/10032291.pdf?arnumber=10032291
Reference47 articles.
1. A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks
2. Transmission Power Control for Over-the-Air Federated Averaging at Network Edge
3. Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis
4. Joint Client Scheduling and Resource Allocation Under Channel Uncertainty in Federated Learning
5. Over-the-Air Computing for Wireless Data Aggregation in Massive IoT
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