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06/13/2023
(Amplified) Banded Matrix Factorization: A unified approach to private training
Matrix factorization (MF) mechanisms for differential privacy (DP) have ...
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05/29/2023
Federated Learning of Gboard Language Models with Differential Privacy
We train language models (LMs) with federated learning (FL) and differen...
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05/29/2023
Unleashing the Power of Randomization in Auditing Differentially Private ML
We present a rigorous methodology for auditing differentially private ma...
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05/20/2023
Can Public Large Language Models Help Private Cross-device Federated Learning?
We study (differentially) private federated learning (FL) of language mo...
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03/17/2023
An Empirical Evaluation of Federated Contextual Bandit Algorithms
As the adoption of federated learning increases for learning from sensit...
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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...
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02/06/2023
One-shot Empirical Privacy Estimation for Federated Learning
Privacy auditing techniques for differentially private (DP) algorithms a...
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12/01/2022
Differentially Private Adaptive Optimization with Delayed Preconditioners
Privacy noise may negate the benefits of using adaptive optimizers in di...
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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...
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11/12/2022
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
We introduce new differentially private (DP) mechanisms for gradient-bas...
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09/21/2020
Training Production Language Models without Memorizing User Data
This paper presents the first consumer-scale next-word prediction (NWP) ...
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07/13/2020
Privacy Amplification via Random Check-Ins
Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fund...
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02/29/2020
Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
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02/18/2020
Is Local SGD Better than Minibatch SGD?
We study local SGD (also known as parallel SGD and federated averaging),...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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11/18/2019
Can You Really Backdoor Federated Learning?
The decentralized nature of federated learning makes detecting and defen...
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11/15/2019
Generative Models for Effective ML on Private, Decentralized Datasets
To improve real-world applications of machine learning, experienced mode...
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05/09/2019
Differentially Private Learning with Adaptive Clipping
We introduce a new adaptive clipping technique for training learning mod...
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04/23/2019
Semi-Cyclic Stochastic Gradient Descent
We consider convex SGD updates with a block-cyclic structure, i.e. where...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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02/04/2019
Towards Federated Learning at Scale: System Design
Federated Learning is a distributed machine learning approach which enab...
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12/18/2018
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Communication on heterogeneous edge networks is a fundamental bottleneck...
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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...
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12/03/2018
LEAF: A Benchmark for Federated Settings
Modern federated networks, such as those comprised of wearable devices, ...
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05/27/2018
cpSGD: Communication-efficient and differentially-private distributed SGD
Distributed stochastic gradient descent is an important subroutine in di...
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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...
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11/14/2016
Practical Secure Aggregation for Federated Learning on User-Held Data
Secure Aggregation protocols allow a collection of mutually distrust par...
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07/01/2016
Deep Learning with Differential Privacy
Machine learning techniques based on neural networks are achieving remar...
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02/25/2010