OLIVE: Oblivious and Differentially Private Federated Learning on Trusted Execution Environment. (arXiv:2202.07165v1 [cs.LG])

By combining Federated Learning with Differential Privacy, it has become
possible to train deep models while taking privacy into account. Using Local
Differential Privacy (LDP) does not require trust in the server, but its
utility is limited due to strong gradient perturbations. On the other hand,
client-level Central Differential Privacy (CDP) provides a good balance between
the privacy and utility of the trained model, but requires trust in the central
server since they have to share raw gradients. We propose …

arxiv environment federated learning learning olive