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%.
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Researchers propose FedQoS, an asynchronous event-triggered federated learning framework for smart vehicles that cuts communication overhead 76.7% and latency 26.0%.
Lower bounds show heterogeneous asynchronous optimization cannot match homogeneous rates under standard similarity assumptions; strong interpolation plus local PL condition closes the gap.
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