DataGuard: Guaranteeing Private Training in Systolic-array Based Accelerators (opens in new tab)

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 ensures that raw sensitive data does not leave the users' devices by training the model locally on the device. DP ensures that the model does not leak any information about an individual by clipping and adding noise to the gradients before updating the model. It provides...