QoS-Aware Federated Learning for Multimodal In-Cabin Interaction in Smart Vehicles
Researchers propose FedQoS, an asynchronous event-triggered federated learning framework for smart vehicles that cuts communication overhead 76.7% and latency 26.0%.
Researchers propose FedQoS, an asynchronous, event-triggered federated learning framework for multimodal in-cabin vehicle systems. It uses a resource-aware training gate and a QoS-aware transmission policy so learning never compromises vehicle mobility or energy reserves, with a staleness-aware proximal term handling update age. On multimodal vehicular datasets it matches FedAvg accuracy while cutting communication overhead by 76.7% and latency cost by 26.0%.
- Two-phase gating decouples local training from uplink communication under heterogeneous vehicular network QoS
- Resource-aware gate starts training only when sensing buffers and energy meet safety thresholds
- Staleness-aware proximal term adjusts global anchor strength based on update age
- Reports 76.7% communication overhead reduction and 26.0% latency cost reduction versus FedAvg
Full article215 words · extracted from arxiv.org · click to collapse
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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.20123