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PAC Privacy Preserving Diffusion Models
Data privacy protection is garnering increased attention among researchers.
DPFormer: Learning Differentially Private Transformer on Long-Tailed Data
The Transformer has emerged as a versatile and effective architecture with broad applications.
Revisiting Hyperparameter Tuning with Differential Privacy
Hyperparameter tuning is a common practice in the application of machine learning but is a typically ignored aspect in the literature on privacy-preserving machine learning due to its negative effect on the overall privacy parameter.
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network connection of things, leading to an advanced AI-driven ecology.
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