2 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.LG2022★ 2 cited
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Shiqi He, Qifan Yan, Feijie Wu +3
Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy. However, the substantial communicat…
cs.CR2021★ 1 cited
Privacy Budget Scheduling
Tao Luo, Mingen Pan, Pierre Tholoniat +3
Machine learning (ML) models trained on personal data have been shown to leak information about users. Differential privacy (DP) enables model training with a guaranteed bound on t…
stat.ML2021
Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning
Mathias Lécuyer
Differential Privacy (DP) is the leading approach to privacy preserving deep learning. As such, there are multiple efforts to provide drop-in integration of DP into popular framewo…