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06/13/2023
(Amplified) Banded Matrix Factorization: A unified approach to private training
Matrix factorization (MF) mechanisms for differential privacy (DP) have ...
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02/02/2023
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning
We study stochastic optimization with linearly correlated noise. Our stu...
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01/18/2023
Federated Automatic Differentiation
Federated learning (FL) is a general framework for learning across heter...
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11/12/2022
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
We introduce new differentially private (DP) mechanisms for gradient-bas...
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02/16/2022
Private Online Prefix Sums via Optimal Matrix Factorizations
Motivated by differentially-private (DP) training of machine learning mo...
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09/08/2021
Iterated Vector Fields and Conservatism, with Applications to Federated Learning
We study iterated vector fields and investigate whether they are conserv...
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02/05/2021
Federated Reconstruction: Partially Local Federated Learning
Personalization methods in federated learning aim to balance the benefit...
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08/14/2020
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning
In this paper we revisit the problem of private empirical risk minimziat...
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02/29/2020
Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
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09/27/2019
