2 papers
cs.AR2026
DataGuard: Guaranteeing Private Training in Systolic-array Based Accelerators
Pawan Kumar Sanjaya, Christina Giannoula, Nikhil Shreekumar +6
Differential privacy (DP) and federated learning (FL) have emerged as important privacy-preserving approaches when using sensitive data to train machine learning (ML) models. FL en…
cs.LG2022
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries
Alexander Cann, Ian Colbert, Ihab Amer
The widespread adoption of deep neural networks in computer vision applications has brought forth a significant interest in adversarial robustness. Existing research has shown that…