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

Federated Reinforcement Learning (FRL) offers a promising solution to various practical challenges in resource allocation for vehicle-to-everything (V2X) networks. However, the data discrepancy among individual agents can significantly degrade the performance of FRL-based algorithms. To address this...

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Bibliographic Details
Published in:IEEE communications letters Vol. 28; no. 12; pp. 2799 - 2803
Main Authors: Xu, Kaidi, Zhou, Shenglong, Ye Li, Geoffrey
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Distributed databases
FRL
Invariants
Machine learning
Neural networks
Observability
Performance evaluation
policy gradient
Receivers
rescale-invariant operation
Resource allocation
Resource management
Training
Trajectory
Transmitters
V2X communications
Vehicle-to-everything
ISSN:1089-7798, 1558-2558
Online Access:Get full text
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Summary:Federated Reinforcement Learning (FRL) offers a promising solution to various practical challenges in resource allocation for vehicle-to-everything (V2X) networks. However, the data discrepancy among individual agents can significantly degrade the performance of FRL-based algorithms. To address this limitation, we exploit the node-wise invariance property of rectified linear unit-activated neural networks, with the aim of reducing data discrepancy to improve learning performance. Based on this property, we introduce a backward rescale-invariant operation to develop a rescale-invariant FRL algorithm. Simulation results demonstrate that the proposed algorithm notably enhances both convergence speed and convergent performance.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2024.3486166