标题： 边缘辅助车联网中能量约束的多任务联邦微调去中心化排名调度 显示英文标题

标题： Decentralized Rank Scheduling for Energy-Constrained Multi-Task Federated Fine-Tuning in Edge-Assisted IoV Networks

摘要： 联邦微调已成为一种有前景的方法，用于在边缘环境中将基础模型（FMs）适应到各种下游任务。 在车联网（IoV）系统中，由于客户端移动性、异构资源和间歇性连接，实现高效且低延迟的多任务适应尤其具有挑战性。 本文提出了一种分层的联邦微调框架，协调路侧单元（RSUs）和车辆，以支持动态IoV场景中的资源感知和移动弹性学习。 利用低秩适应（LoRA），我们引入了一种去中心化的、节能的秩适应机制，将其形式化为一个约束的多臂老虎机问题。 开发了一种新颖的UCB-DUAL算法，以在每任务能源预算下实现自适应探索，实现了可证明的次线性遗憾。 为了评估我们的方法，我们构建了一个基于真实世界轨迹的大规模IoV模拟器，捕捉动态参与、RSU切换和通信变化。 大量实验表明，我们的方法在所有基线中实现了最佳的准确性-效率权衡，延迟降低了超过24%，平均准确率提高了2.5%以上。 显示英文摘要

摘要： Federated fine-tuning has emerged as a promising approach for adapting foundation models (FMs) to diverse downstream tasks in edge environments. In Internet of Vehicles (IoV) systems, enabling efficient and low-latency multi-task adaptation is particularly challenging due to client mobility, heterogeneous resources, and intermittent connectivity. This paper proposes a hierarchical federated fine-tuning framework that coordinates roadside units (RSUs) and vehicles to support resource-aware and mobility-resilient learning across dynamic IoV scenarios. Leveraging Low-Rank Adaptation (LoRA), we introduce a decentralized, energy-aware rank adaptation mechanism formulated as a constrained multi-armed bandit problem. A novel UCB-DUAL algorithm is developed to enable adaptive exploration under per-task energy budgets, achieving provable sublinear regret. To evaluate our method, we construct a large-scale IoV simulator based on real-world trajectories, capturing dynamic participation, RSU handoffs, and communication variability. Extensive experiments show that our approach achieves the best accuracy-efficiency trade-off among all baselines, reducing latency by over 24\% and improving average accuracy by more than 2.5\%.