  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.
