Search Results for author: H. Brendan McMahan

Found 26 papers, 9 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

Adaptive Federated Optimization

3 code implementations ICLR 2021 Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, H. Brendan McMahan

Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data.

Federated Learning

Is Local SGD Better than Minibatch SGD?

no code implementations ICML 2020 Blake Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro

We study local SGD (also known as parallel SGD and federated averaging), a natural and frequently used stochastic distributed optimization method.

Distributed Optimization

Can You Really Backdoor Federated Learning?

no code implementations18 Nov 2019 Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan

This paper focuses on backdoor attacks in the federated learning setting, where the goal of the adversary is to reduce the performance of the model on targeted tasks while maintaining good performance on the main task.

Federated Learning

Generative Models for Effective ML on Private, Decentralized Datasets

2 code implementations ICLR 2020 Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas

To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact.

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

Semi-Cyclic Stochastic Gradient Descent

no code implementations23 Apr 2019 Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar

We consider convex SGD updates with a block-cyclic structure, i. e. where each cycle consists of a small number of blocks, each with many samples from a possibly different, block-specific, distribution.

Federated Learning

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

no code implementations ICLR 2019 Sebastian Caldas, Jakub Konečny, H. Brendan McMahan, Ameet Talwalkar

Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation.

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.

LEAF: A Benchmark for Federated Settings

5 code implementations3 Dec 2018 Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar

Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.

Autonomous Vehicles Federated Learning +2

Learning Differentially Private Recurrent Language Models

no code implementations ICLR 2018 H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy.

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

no code implementations26 Aug 2017 Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang

The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy.

Practical Secure Aggregation for Federated Learning on User-Held Data

no code implementations14 Nov 2016 Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth

Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves.

Federated Learning

Distributed Mean Estimation with Limited Communication

no code implementations ICML 2017 Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan

Motivated by the need for distributed learning and optimization algorithms with low communication cost, we study communication efficient algorithms for distributed mean estimation.


Federated Learning: Strategies for Improving Communication Efficiency

no code implementations ICLR 2018 Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon

We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model.

Federated Learning Quantization

Deep Learning with Differential Privacy

18 code implementations1 Jul 2016 Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang

Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains.

Communication-Efficient Learning of Deep Networks from Decentralized Data

14 code implementations17 Feb 2016 H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas

Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device.

A Survey of Algorithms and Analysis for Adaptive Online Learning

no code implementations14 Mar 2014 H. Brendan McMahan

Further, we prove a general and exact equivalence between an arbitrary adaptive Mirror Descent algorithm and a correspond- ing FTRL update, which allows us to analyze any Mirror Descent algorithm in the same framework.

online learning

Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations

no code implementations3 Mar 2014 H. Brendan McMahan, Francesco Orabona

When $T$ is known, we derive an algorithm with an optimal regret bound (up to constant factors).

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