arXiv：cs.LG· Farhoud Jafari Kaleibar, Amr M. Zaki, Marin Litoiu·· 3 小时前
面向资源受限车联网的自适应多判别器 WGAN 框架：结合强化学习与博弈论
Adaptive Multi-Discriminator WGAN Framework for Resource-Constrained Internet of Vehicles Using Reinforcement Learning and Game Theory
AI 导读
研究者提出自适应多判别器 Wasserstein GAN（MD-WGAN）框架，将强化学习与博弈论协调结合，用于资源受限的车联网环境。路边单元托管生成器并配对 DQN 智能体，由其选择判别器子集并管理跨移动车载节点的分布式训练，博弈论协调步骤则分配生成器与判别器之间的训练轮次。
正文
Abstract:Managing machine learning workloads as a network service introduces a resource-orchestration problem distinct from conventional model training; which nodes should be allocated to a task, how communication and computation budgets should be divided among them, and how service quality should be sustained as connectivity and node availability change with mobility. Deploying Generative Adversarial Networks (GANs) in Internet of Vehicles (IoV) environments is a demanding instance of this problem; resource constraints, dynamic network topologies, and competing optimization objectives mean that traditional GAN architectures cannot simultaneously achieve high accuracy, efficient resource use, low delay, and low communication overhead. This paper introduces an adaptive multi-discriminator Wasserstein GAN (MD-WGAN) framework that integrates reinforcement learning with game-theoretic coordination to address these challenges jointly. In our framework, roadside units host generators paired with Deep Q-Network (DQN) agents that select discriminator subsets and manage distributed training across mobile vehicular nodes, while a game-theoretic coordination step allocates training epochs between generators and discriminators. A unified optimization objective ties adversarial learning quality to resource efficiency, communication overhead, and latency under vehicular constraints, allowing the framework to continuously adapt its training behavior as network conditions change. Evaluation on real-world NGSIM trajectory data shows that the framework attains prediction accuracy comparable to state-of-the-art GAN baselines - the lowest RMSE (1.029) and MAE (0.894) among all evaluated methods - while markedly improving resource efficiency: average CPU utilization is reduced by roughly 28% and mean memory usage by roughly 6%, at competitive communication overhead and latency.
| Comments: | 15 pages |
| Subjects: | Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC) |
| Cite as: | arXiv:2610.10926 [cs.LG] |
| (or arXiv:2610.10926v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10926 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Farhoud Jafari Kaleibar [view email]
[v1]
Wed, 7 Oct 2026 21:28:46 UTC (7,561 KB)
来源：arXiv：cs.LG · arxiv.org