Search Results for author: Galen Andrew

Found 8 papers, 6 papers with code

One-shot Empirical Privacy Estimation for Federated Learning

1 code implementation6 Feb 2023 Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith M. Suriyakumar

Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings where known analytical bounds are not tight.

Federated Learning

Training Production Language Models without Memorizing User Data

no code implementations21 Sep 2020 Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, Françoise Beaufays

This paper presents the first consumer-scale next-word prediction (NWP) model trained with Federated Learning (FL) while leveraging the Differentially Private Federated Averaging (DP-FedAvg) technique.

Federated Learning Memorization

Differentially Private Learning with Adaptive Clipping

1 code implementation NeurIPS 2021 Galen Andrew, Om Thakkar, H. Brendan McMahan, Swaroop Ramaswamy

Existing approaches for training neural networks with user-level differential privacy (e. g., DP Federated Averaging) in federated learning (FL) settings involve bounding the contribution of each user's model update by clipping it to some constant value.

Federated Learning

A General Approach to Adding Differential Privacy to Iterative Training Procedures

4 code implementations15 Dec 2018 H. Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, Peter Kairouz

In this work we address the practical challenges of training machine learning models on privacy-sensitive datasets by introducing a modular approach that minimizes changes to training algorithms, provides a variety of configuration strategies for the privacy mechanism, and then isolates and simplifies the critical logic that computes the final privacy guarantees.

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