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Found 5 papers, 1 papers with code
Training Differentially Private Ad Prediction Models with Semi-Sensitive Features
Motivated by problems arising in digital advertising, we introduce the task of training differentially private (DP) machine learning models with semi-sensitive features.
Private Ad Modeling with DP-SGD
A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descent (DP-SGD).
Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments
However, the overhead of blinding and unblinding the data is a limiting factor to scalability.
Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Most of the model training is performed on high performance compute nodes and the training data is stored near these nodes for faster training.
Collage Inference: Using Coded Redundancy for Low Variance Distributed Image Classification
Deploying the collage-cnn models in the cloud, we demonstrate that the 99th percentile tail latency of inference can be reduced by 1. 2x to 2x compared to replication based approaches while providing high accuracy.
