A server-side personalized aggregation method for federated learning reduces 10-second vehicle speed prediction error by 0.8% over eleven baselines on a simulated urban driving dataset.
Short-term vehicle speed prediction based on convolutional bidirectional lstm networks,
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FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
A server-side personalized aggregation method for federated learning reduces 10-second vehicle speed prediction error by 0.8% over eleven baselines on a simulated urban driving dataset.