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School of Artificial Intelligence, Beihang University
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FedSEPT achieves superior local adaptation without sacrificing global generalization, even under stringent privacy conditions.
Hallucinated outputs trigger unique gradient patterns in LLMs, enabling AURORA to achieve superior detection performance across diverse tasks and models.
By intelligently perturbing class prototypes based on their discriminative power, VPDR achieves a superior privacy-utility trade-off in federated learning compared to naive Gaussian noise.
Transfer learning can unlock scalable emission control across diverse waste incineration plants by learning transferable system-level structures that capture physical constraints, operating-regime heterogeneity, and carbon-pollutant coupling.
LLMs can bridge the gap between heterogeneous blockchain data to detect fraud with significantly improved accuracy, even in zero-shot cross-chain scenarios.
Forget likelihood scores and fine-tuning tricks: LLMs leak pre-training data membership through subtle, yet detectable, deviations in their gradient updates.
