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Prompt Public Large Language Models to Synthesize Data for Private On-device Applications
Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP).
Federated Learning of Gboard Language Models with Differential Privacy
We train language models (LMs) with federated learning (FL) and differential privacy (DP) in the Google Keyboard (Gboard).
Learning Non-Stationary Time-Series with Dynamic Pattern Extractions
Our results show that our model can predict 4-hour future trends with high accuracy in the Forex dataset, which is crucial in realistic scenarios to assist foreign exchange trading decision making.
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction
Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.
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