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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Veröffentlicht in:IEEE communications letters Jg. 28; H. 12; S. 2799 - 2803
Hauptverfasser: Xu, Kaidi, Zhou, Shenglong, Ye Li, Geoffrey
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2024.3486166