H. Brendan McMahan

research

∙ 06/13/2023

(Amplified) Banded Matrix Factorization: A unified approach to private training

Matrix factorization (MF) mechanisms for differential privacy (DP) have ...

0 Christopher A. Choquette-Choo, et al. ∙

research

∙ 05/29/2023

Federated Learning of Gboard Language Models with Differential Privacy

We train language models (LMs) with federated learning (FL) and differen...

0 Zheng Xu, et al. ∙

research

∙ 05/29/2023

Unleashing the Power of Randomization in Auditing Differentially Private ML

We present a rigorous methodology for auditing differentially private ma...

0 Krishna Pillutla, et al. ∙

research

∙ 05/20/2023

Can Public Large Language Models Help Private Cross-device Federated Learning?

We study (differentially) private federated learning (FL) of language mo...

0 Boxin Wang, et al. ∙

research

∙ 03/17/2023

An Empirical Evaluation of Federated Contextual Bandit Algorithms

As the adoption of federated learning increases for learning from sensit...

0 Alekh Agarwal, et al. ∙

research

∙ 03/01/2023

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

ML models are ubiquitous in real world applications and are a constant f...

0 Natalia Ponomareva, et al. ∙

research

∙ 02/06/2023

One-shot Empirical Privacy Estimation for Federated Learning

Privacy auditing techniques for differentially private (DP) algorithms a...

0 Galen Andrew, et al. ∙

research

∙ 12/01/2022

Differentially Private Adaptive Optimization with Delayed Preconditioners

Privacy noise may negate the benefits of using adaptive optimizers in di...

0 Tian Li, et al. ∙

research

∙ 11/20/2022

Learning to Generate Image Embeddings with User-level Differential Privacy

Small on-device models have been successfully trained with user-level di...

0 Zheng Xu, et al. ∙

research

∙ 11/12/2022

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning

We introduce new differentially private (DP) mechanisms for gradient-bas...

0 Christopher A. Choquette-Choo, et al. ∙

research

∙ 09/21/2020

Training Production Language Models without Memorizing User Data

This paper presents the first consumer-scale next-word prediction (NWP) ...

0 Swaroop Ramaswamy, et al. ∙

research

∙ 07/13/2020

Privacy Amplification via Random Check-Ins

Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fund...

12 Borja Balle, et al. ∙

research

∙ 02/29/2020

Adaptive Federated Optimization

Federated learning is a distributed machine learning paradigm in which a...

28 Sashank Reddi, et al. ∙

research

∙ 02/18/2020

Is Local SGD Better than Minibatch SGD?

We study local SGD (also known as parallel SGD and federated averaging),...

5 Blake Woodworth, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

research

∙ 11/18/2019

Can You Really Backdoor Federated Learning?

The decentralized nature of federated learning makes detecting and defen...

0 Ziteng Sun, et al. ∙

research

∙ 11/15/2019

Generative Models for Effective ML on Private, Decentralized Datasets

To improve real-world applications of machine learning, experienced mode...

29 Sean Augenstein, et al. ∙

research

∙ 05/09/2019

Differentially Private Learning with Adaptive Clipping

We introduce a new adaptive clipping technique for training learning mod...

0 Om Thakkar, et al. ∙

research

∙ 04/23/2019

Semi-Cyclic Stochastic Gradient Descent

We consider convex SGD updates with a block-cyclic structure, i.e. where...

0 Hubert Eichner, et al. ∙

research

∙ 03/29/2019

SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...

0 Alexander Ratner, et al. ∙

research

∙ 02/04/2019

Towards Federated Learning at Scale: System Design

Federated Learning is a distributed machine learning approach which enab...

0 Keith Bonawitz, et al. ∙

research

∙ 12/18/2018

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Communication on heterogeneous edge networks is a fundamental bottleneck...

10 Sebastian Caldas, et al. ∙

research

∙ 12/15/2018

A General Approach to Adding Differential Privacy to Iterative Training Procedures

In this work we address the practical challenges of training machine lea...

0 H. Brendan McMahan, et al. ∙

research

∙ 12/03/2018

LEAF: A Benchmark for Federated Settings

Modern federated networks, such as those comprised of wearable devices, ...

0 Sebastian Caldas, et al. ∙

research

∙ 05/27/2018

cpSGD: Communication-efficient and differentially-private distributed SGD

Distributed stochastic gradient descent is an important subroutine in di...

0 Naman Agarwal, et al. ∙

research

∙ 08/26/2017

On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches

The recent, remarkable growth of machine learning has led to intense int...

0 Martín Abadi, et al. ∙

research

∙ 11/14/2016

Practical Secure Aggregation for Federated Learning on User-Held Data

Secure Aggregation protocols allow a collection of mutually distrust par...

0 Keith Bonawitz, et al. ∙

research

∙ 07/01/2016

Deep Learning with Differential Privacy

Machine learning techniques based on neural networks are achieving remar...

0 Martín Abadi, et al. ∙

research

∙ 02/25/2010

Less Regret via Online Conditioning

We analyze and evaluate an online gradient descent algorithm with adapti...

0 Matthew Streeter, et al. ∙

H. Brendan McMahan

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