Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks

Kaidi Xu; Shenglong Zhou; Geoffrey Ye Li

doi:10.1109/lcomm.2024.3486166

ScienceGate Book Chapters

JOURNAL ARTICLE

Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks

Kaidi Xu Shenglong Zhou Geoffrey Ye Li

Year: 2024 Journal: IEEE Communications Letters Vol: 28 (12)Pages: 2799-2803 Publisher: IEEE Communications Society

DOI: 10.1109/lcomm.2024.3486166

Get Full-Text PDF Get Analytical Report

Keywords:

Reinforcement learning Computer science Invariant (physics) Resource allocation Resource management (computing) Artificial intelligence Distributed computing Computer network Mathematics

Metrics

Cited By

1.67

FWCI (Field Weighted Citation Impact)

Refs

0.76

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

Age of Information Optimization

Physical Sciences → Computer Science → Computer Networks and Communications

Software-Defined Networks and 5G

Physical Sciences → Computer Science → Computer Networks and Communications

Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks

Metrics

Citation History

Topics

Related Documents

Federated Reinforcement Learning for Resource Allocation in V2X Networks

Federated Reinforcement Learning for Resource Allocation in V2X Networks

Federated Deep Reinforcement Learning-based Resource Allocation in Heterogeneous Networks

Meta Federated Reinforcement Learning for Distributed Resource Allocation

Resource Allocation in Vehicular Networks Based on Federated Multi-Agent Reinforcement Learning