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Found 6 papers, 0 papers with code
Resampling methods for private statistical inference
We consider the task of constructing confidence intervals with differential privacy.
Differentially Private Heavy Hitter Detection using Federated Analytics
In this work, we study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection.
Private optimization in the interpolation regime: faster rates and hardness results
In non-private stochastic convex optimization, stochastic gradient methods converge much faster on interpolation problems -- problems where there exists a solution that simultaneously minimizes all of the sample losses -- than on non-interpolating ones; we show that generally similar improvements are impossible in the private setting.
Federated Asymptotics: a model to compare federated learning algorithms
We propose an asymptotic framework to analyze the performance of (personalized) federated learning algorithms.
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization
We extend the Approximate-Proximal Point (aProx) family of model-based methods for solving stochastic convex optimization problems, including stochastic subgradient, proximal point, and bundle methods, to the minibatch and accelerated setting.
Minibatch Stochastic Approximate Proximal Point Methods
In contrast to standard stochastic gradient methods, these methods may have linear speedup in the minibatch setting even for non-smooth functions.
