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

arXiv: https://arxiv.org/abs/2405.01961 · HTML: https://mlsanity.com/paper/2405.01961/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2024.

## Built on by (most cited first, 1 of 1)

- [Joint Channel Selection using FedDRL in V2X](https://mlsanity.com/paper/2410.20687/) (2024)

## Abstract (arXiv)

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 ReLU-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.