# Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2407.02342 · HTML: https://mlsanity.com/paper/2407.02342/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 60 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated Reinforcement Learning: Techniques, Applications, and Open Challenges](https://mlsanity.com/paper/2108.11887/) (2021)
- [Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning](https://mlsanity.com/paper/2208.01219/) (2022)
- [High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks](https://mlsanity.com/paper/2208.01890/) (2022)
- [Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method](https://mlsanity.com/paper/2208.01283/) (2022)
- [Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons](https://mlsanity.com/paper/2309.10234/) (2023)
- [URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing](https://mlsanity.com/paper/2311.18352/) (2023)
- [Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network](https://mlsanity.com/paper/2401.09886/) (2024)
- [A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications](https://mlsanity.com/paper/1703.10750/) (2017)
- [GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds](https://mlsanity.com/paper/2307.00777/) (2023)
- [Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks](https://mlsanity.com/paper/2009.06681/) (2020)
- [Stochastic Graph Neural Network-based Value Decomposition for MARL in Internet of Vehicles](https://mlsanity.com/paper/2303.13213/) (2023)

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

- [A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning](https://mlsanity.com/paper/2410.07881/) (2024)

## Abstract (arXiv)

With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications. This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints. We adopt a Multi-agent Deep Reinforcement Learning (MADRL) approach, allowing vehicles to autonomously make optimal data offloading decisions. However, MADRL poses risks of vehicle information leakage during communication learning and centralized training. To mitigate this, we employ a Federated Learning (FL) framework that shares model parameters instead of raw data to protect the privacy of vehicle users. Building on this, we propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL), to optimize AoI across the system. For the first time, road scenarios are constructed as graph data structures, and a GNN-based federated learning framework is proposed, effectively combining distributed and centralized federated aggregation. Furthermore, we propose a new MADRL algorithm that simplifies decision making and enhances offloading efficiency, further reducing the decision complexity. Simulation results demonstrate the superiority of our proposed approach to other methods through simulations.