智能车辆中多模态座舱交互的QoS感知联邦学习

中文摘要 AI 辅助

现代智能车辆利用多模态传感器，从高带宽视觉系统到低速率生理监测器，以提供个性化的座舱内服务。然而，将高保真多模态融合与协作训练相结合，常常受到车载网络异构且时变的服务质量（QoS）约束的阻碍。标准联邦学习（FL）方法强制实施刚性的同步轮次，未能考虑这些资源不对称性，导致安全关键的时序违规和能量耗尽。在本文中，我们提出FedQoS，一种新颖的异步、事件触发的联邦学习框架，通过两阶段门控机制将本地计算与全局通信解耦。首先，我们引入一个资源感知的训练门控，仅在感知缓冲区和能量储备满足安全阈值时初始化本地学习，防止机器学习任务损害核心车辆移动性。其次，一种QoS感知的传输策略基于效率分数门控上行链路更新，该分数平衡模型新颖性与瞬时延迟和能量成本。在本地，客户端优化一个包含陈旧性感知近端项的目标函数，该函数根据更新年龄动态调整全局锚点强度。在多模态车载数据集上的大量实验表明，与FedAvg相比，FedQoS在实现具有竞争力的个性化准确率的同时，仅产生边际性能损失，同时大幅减少QoS违规，将通信开销削减76.7%，并将延迟成本降低26.0%，展示了在实际车载部署中非常有利的准确性与效率平衡。

英文摘要

Modern smart vehicles leverage multimodal sensors, ranging from high-bandwidth vision systems to low-rate physiological monitors, to provide personalized in-cabin services. However, integrating high-fidelity multimodal fusion with collaborative training is often hindered by the heterogeneous and time-varying Quality of Service (QoS) constraints of vehicular networks. Standard Federated Learning (FL) approaches enforce rigid synchronous rounds that fail to account for these resource asymmetries, leading to safety-critical timing violations and energy exhaustion. In this paper, we propose FedQoS, a novel asynchronous, event-triggered FL framework that decouples local computation from global communication via a two-phase gating mechanism. First, we introduce a resource-aware training gate that initializes local learning only when sensing buffers and energy reserves meet safety thresholds, preventing ML tasks from compromising core vehicle mobility. Second, a QoS-aware transmission policy gates uplink updates based on an efficiency score that balances model novelty against instantaneous latency and energy costs. Locally, clients optimize an objective featuring a staleness-aware proximal term that dynamically adjusts the global anchor strength based on update age. Extensive experiments on multimodal vehicular datasets demonstrate that FedQoS achieves competitive personalized accuracy with only marginal performance loss compared to FedAvg, while substantially reducing QoS violations, cutting communication overhead by 76.7\%, and lowering latency cost by 26.0\%, demonstrating a highly favorable accuracy and efficiency balance for real-world vehicular deployments.

发表机构

• University of Stuttgart（斯图加特大学）
• Polish Academy of Sciences（波兰科学院）
• Ideatrum Ltd.（Ideatrum有限公司）

机构由 AI 辅助整理，请以论文原文为准。