Resource Consumption for Supporting Federated Learning in Wireless Networks
Author:
Affiliation:
1. National Key Laboratory on Communications, University of Electronic Science and Technology of China, Chengdu, China
2. James Watt School of Engineering, University of Glasgow, Glasgow, U.K.
Funder
National Science Foundation of China
Huawei Cooperation Project
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications
Link
http://xplorestaging.ieee.org/ielx7/7693/9944944/09798758.pdf?arnumber=9798758
Reference37 articles.
1. Convergence analysis of two-layer neural networks with ReLU activation;li;Proc 31st Int Conf Neural Inf Process Syst,2017
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3. Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach
4. Access Control for RAN Slicing based on Federated Deep Reinforcement Learning
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