Distributed Joint Resource Optimization for Federated Learning Task Distribution
Author:
Affiliation:
1. School of Information and Electronic Engineering, Advanced Institute of Industrial Technology, Tokyo, Japan
2. Department of Information and Electronic Engineering, Muroran Institute of Technology, Muroran, Japan
Funder
JSPS KAKENHI
Leading Initiative for Excellent Young Researchers (LEADER), MEXT, Japan
JST, PRESTO, Japan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Renewable Energy, Sustainability and the Environment
Link
http://xplorestaging.ieee.org/ielx7/7511293/10224556/10083269.pdf?arnumber=10083269
Reference45 articles.
1. Toward an Intelligent Edge: Wireless Communication Meets Machine Learning
2. One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
3. Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network
4. Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification
5. Over-the-Air Computation Systems: Optimization, Analysis and Scaling Laws
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