LA-LoRA decouples LoRA matrix updates in DPFL settings to improve robustness to privacy noise, delivering up to 16.83% higher accuracy than prior LoRA variants on Swin-B under strict epsilon=1.
Learn from global correlations: Enhancing evolutionary algorithm via spectral gnn
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FAST uses a Temporal-Spatial-Temporal structure with attention and Mamba modules plus learnable embeddings to achieve better accuracy on traffic prediction tasks than previous models.
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Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
LA-LoRA decouples LoRA matrix updates in DPFL settings to improve robustness to privacy noise, delivering up to 16.83% higher accuracy than prior LoRA variants on Swin-B under strict epsilon=1.
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FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction
FAST uses a Temporal-Spatial-Temporal structure with attention and Mamba modules plus learnable embeddings to achieve better accuracy on traffic prediction tasks than previous models.