Search Results for author: Galen Andrew

Found 6 papers, 2 papers with code

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

Differentially Private Learning with Adaptive Clipping

no code implementations 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.

Applied Federated Learning: Improving Google Keyboard Query Suggestions

no code implementations7 Dec 2018 Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, Françoise Beaufays

Federated learning is a distributed form of machine learning where both the training data and model training are decentralized.

Federated Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.