7 papers · 1 filter
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
Martin Pelikan, Sheikh Shams Azam, Vitaly Feldman +4
While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to…
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
Guy Kornowski, Daogao Liu, Kunal Talwar
We study differentially private (DP) optimization algorithms for stochastic and empirical objectives which are neither smooth nor convex, and propose methods that return a Goldstei…
Faster Rates for Private Adversarial Bandits
Hilal Asi, Vinod Raman, Kunal Talwar
We design new differentially private algorithms for the problems of adversarial bandits and bandits with expert advice. For adversarial bandits, we give a simple and efficient conv…
On Privately Estimating a Single Parameter
Hilal Asi, John C. Duchi, Kunal Talwar
We investigate differentially private estimators for individual parameters within larger parametric models. While generic private estimators exist, the estimators we provide repose…
Adaptive Batch Size for Privately Finding Second-Order Stationary Points
Daogao Liu, Kunal Talwar
There is a gap between finding a first-order stationary point (FOSP) and a second-order stationary point (SOSP) under differential privacy constraints, and it remains unclear wheth…
Private Online Learning via Lazy Algorithms
Hilal Asi, Tomer Koren, Daogao Liu +1
We study the problem of private online learning, specifically, online prediction from experts (OPE) and online convex optimization (OCO). We propose a new transformation that trans…