2 papers
cs.LG2024
One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew, Peter Kairouz, Sewoong Oh +3
Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings wher…
cs.LG2024
Can Public Large Language Models Help Private Cross-device Federated Learning?
Boxin Wang, Yibo Jacky Zhang, Yuan Cao +5
We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful form…