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Found 6 papers, 1 papers with code
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.
WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning
The experimental results demonstrate that WrapperFL can be successfully applied to a wide range of applications under practical settings and improves the local model with federated learning at a low cost.
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.
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN
However, it is challenging for GANs to model distributions of separate non-i. i. d.
Real-World Image Datasets for Federated Learning
Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private.
