23 citations · 37 across the 4 of their papers we have counts for
4 papers
Pruning Compact ConvNets for Efficient Inference
Sayan Ghosh, Karthik Prasad, Xiaoliang Dai +4
Neural network pruning is frequently used to compress over-parameterized networks by large amounts, while incurring only marginal drops in generalization performance. However, the…
Reconciling Security and Communication Efficiency in Federated Learning
Karthik Prasad, Sayan Ghosh, Graham Cormode +3
Cross-device Federated Learning is an increasingly popular machine learning setting to train a model by leveraging a large population of client devices with high privacy and securi…
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles +9
We introduce Opacus, a free, open-source PyTorch library for training deep learning models with differential privacy (hosted at opacus.ai). Opacus is designed for simplicity, flexi…
Antipodes of Label Differential Privacy: PATE and ALIBI
Mani Malek, Ilya Mironov, Karthik Prasad +2
We consider the privacy-preserving machine learning (ML) setting where the trained model must satisfy differential privacy (DP) with respect to the labels of the training examples.…