# Federated Reinforcement Learning for Resource Allocation in V2X Networks

arXiv: https://arxiv.org/abs/2310.09858 · HTML: https://mlsanity.com/paper/2310.09858/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 15 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data](https://mlsanity.com/paper/2005.11418/) (2020)
- [Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games](https://mlsanity.com/paper/2106.01969/) (2021)
- [Federated Reinforcement Learning: Techniques, Applications, and Open Challenges](https://mlsanity.com/paper/2108.11887/) (2021)
- [Federated Learning via Inexact ADMM](https://mlsanity.com/paper/2204.10607/) (2022)
- [Federated Learning for Ultra-Reliable Low-Latency V2V Communications](https://mlsanity.com/paper/1805.09253/) (2018)
- [Communication-Efficient ADMM-based Federated Learning](https://mlsanity.com/paper/2110.15318/) (2021)
- [A Survey on Resource Allocation in Vehicular Networks](https://mlsanity.com/paper/1909.13587/) (2019)
- [FedGiA: An Efficient Hybrid Algorithm for Federated Learning](https://mlsanity.com/paper/2205.01438/) (2022)
- [Vehicular Communications: A Network Layer Perspective](https://mlsanity.com/paper/1707.09972/) (2017)
- [Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2112.14709/) (2021)
- [Dynamic Proximity-aware Resource Allocation in Vehicle-to-Vehicle (V2V) Communications](https://mlsanity.com/paper/1609.03717/) (2016)

## Built on by (most cited first, 3 of 3)

- [BADM: Batch ADMM for Deep Learning](https://mlsanity.com/paper/2407.01640/) (2024)
- [Preconditioned Inexact Stochastic ADMM for Deep Model](https://mlsanity.com/paper/2502.10784/) (2025)
- [Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking](https://mlsanity.com/paper/2603.06607/) (2026)

## Abstract (arXiv)

Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning (e.g., reinforcement learning). In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL). On one hand, the usage of RL overcomes many challenges from the model-based optimization schemes. On the other hand, federated learning (FL) enables agents to deal with a number of practical issues, such as privacy, communication overhead, and exploration efficiency. The framework of FRL is then implemented by the inexact alternative direction method of multipliers (ADMM), where subproblems are solved approximately using policy gradients and accelerated by an adaptive step size calculated from their second moments. The developed algorithm, PASM, is proven to be convergent under mild conditions and has a nice numerical performance compared with some baseline methods for solving the resource allocation problem in a V2X network.