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We propose a new privacy-preserving learning framework, outsourcing training to cloud without uploading data, which provides more data without injecting noise into gradient or samples.

Protecting privacy in learning while maintaining the model performance has become increasingly critical in many applications that involve sensitive data. Private Gradient Descent (PGD) is a commonly used private learning framework, which noises …

Efficient and federated learning for heterogeneous clients with different memory sizes

A distributed domain/group debiasing framework for unsupervised domain adaptation or fairness enhancement.

Early identification and accurate assessment of Mild Cognitive Impairment (MCI) is critical for clinical-trial enrichment as well as the early intervention of the neurodegenerative disease. Continuous home-based measurements of functions using simple …

On the concern of data privacy, we aim to develop algorithms towards learning accurate models privately from data.

We aim to early detect and intervene dementia diseases leveraging the power of (Generative) AI.