Large-scale Internet of Vehicles (IoV) deployments increasingly demand the on-device adaptation of foundation models to support diverse, mission-critical perception tasks. While federated fine-tuning offers a promising solution for efficient model specialization, existing approaches often struggle to reconcile the inherent conflict between stringent global energy budgets, heterogeneous task demands, and the high volatility of vehicular network connectivity. In this work, we introduce a hierarchical, adaptive framework that decouples multi-task fine-tuning into two interdependent optimization phases. First, we implement a feedback-loop mechanism at the infrastructure level that dynamically redistributes global energy budgets across concurrent tasks based on real-time convergence dynamics and resource utilization. Second, at the vehicle level, we formulate intra-task rank selection as an energy-constrained online learning problem, solved via a novel primal-dual bandit algorithm, UCB-DUAL, which provides theoretical guarantees on sublinear regret. Our approach effectively internalizes global energy constraints into local decision-making, allowing vehicles to autonomously navigate the complex trade-off between model accuracy, latency, and power consumption. Empirical evaluations using a large-scale IoV simulator, driven by real-world trajectory data, confirm that our proposed method significantly outperforms current federated fine-tuning baselines, offering a robust and scalable solution for resource-constrained vehicular intelligence.

翻译：大规模车联网部署日益要求对基础模型进行设备端适配，以支持多样化的关键感知任务。尽管联邦微调为高效模型特化提供了有前景的解决方案，但现有方法往往难以协调严格全局能量预算、异构任务需求与车辆网络连接高波动性之间的固有冲突。本文提出一种分层的自适应框架，将多任务微调解耦为两个相互依赖的优化阶段。首先，在基础设施层面实施反馈循环机制，基于实时收敛动态与资源利用率，在并发任务间动态重新分配全局能量预算。其次，在车辆层面，将任务内秩选择问题建模为能量约束的在线学习问题，并通过新型原始-对偶赌徒算法UCB-DUAL求解，该算法具有次线性遗憾的理论保证。本方法将全局能量约束有效内化至局部决策，使车辆能自主权衡模型精度、延迟与功耗的复杂关系。基于真实轨迹数据驱动的大规模车联网仿真器实证评估表明，所提方法显著优于现有联邦微调基线，为资源受限的车载智能提供了鲁棒且可扩展的解决方案。