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
From the original article
1 min read at arxiv.orgModern 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.
By Baran Can Gül · arxiv.org
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