5 papers · 1 filter
The Cost of Adaptation under Differential Privacy: Optimal Adaptive Federated Density Estimation
T. Tony Cai, Abhinav Chakraborty, Lasse Vuursteen
Privacy-preserving data analysis has become a central challenge in modern statistics. At the same time, a long-standing goal in statistics is the development of adaptive procedures…
Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints
Tony Cai, Abhinav Chakraborty, Lasse Vuursteen
Federated learning (FL) is a distributed machine learning technique designed to preserve data privacy and security, and it has gained significant importance due to its broad range…
Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime
Lasse Vuursteen
We study distributed goodness-of-fit testing for discrete distribution under bandwidth and differential privacy constraints. Information constraint distributed goodness-of-fit test…
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints
T. Tony Cai, Abhinav Chakraborty, Lasse Vuursteen
This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy co…
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests
T. Tony Cai, Abhinav Chakraborty, Lasse Vuursteen
Federated learning has attracted significant recent attention due to its applicability across a wide range of settings where data is collected and analyzed across disparate locatio…