2 code implementations • 26 Feb 2021 • Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
We consider training models with differential privacy (DP) using mini-batch gradients.
1 code implementation • 16 Feb 2022 • Sergey Denisov, Brendan Mcmahan, Keith Rush, Adam Smith, Abhradeep Guha Thakurta
Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting.
2 code implementations • 16 Apr 2024 • Hubert Eichner, Daniel Ramage, Kallista Bonawitz, Dzmitry Huba, Tiziano Santoro, Brett McLarnon, Timon Van Overveldt, Nova Fallen, Peter Kairouz, Albert Cheu, Katharine Daly, Adria Gascon, Marco Gruteser, Brendan Mcmahan
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data.
no code implementations • NeurIPS 2018 • Blake Woodworth, Jialei Wang, Adam Smith, Brendan Mcmahan, Nathan Srebro
We suggest a general oracle-based framework that captures different parallel stochastic optimization settings described by a dependency graph, and derive generic lower bounds in terms of this graph.
no code implementations • 11 Nov 2015 • Jakub Konečný, Brendan Mcmahan, Daniel Ramage
We refer to this setting as Federated Optimization.
no code implementations • NeurIPS 2014 • Brendan Mcmahan, Matthew Streeter
We analyze new online gradient descent algorithms for distributed systems with large delays between gradient computations and the corresponding updates.
no code implementations • NeurIPS 2013 • Brendan Mcmahan, Jacob Abernethy
We design and analyze minimax-optimal algorithms for online linear optimization games where the player's choice is unconstrained.
no code implementations • NeurIPS 2013 • John Duchi, Michael. I. Jordan, Brendan Mcmahan
We study stochastic optimization problems when the \emph{data} is sparse, which is in a sense dual to the current understanding of high-dimensional statistical learning and optimization.
no code implementations • NeurIPS 2012 • Brendan Mcmahan, Matthew Streeter
We present an algorithm that, without such prior knowledge, offers near-optimal regret bounds with respect to _any_ choice of x*.
no code implementations • 30 Nov 2019 • Keith Bonawitz, Fariborz Salehi, Jakub Konečný, Brendan Mcmahan, Marco Gruteser
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud.
no code implementations • 19 Aug 2022 • Zachary Charles, Kallista Bonawitz, Stanislav Chiknavaryan, Brendan Mcmahan, Blaise Agüera y Arcas
In order to make this practical, we outline a primitive, federated select, which enables client-specific selection in realistic FL systems.
no code implementations • NeurIPS 2023 • Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan Mcmahan
We propose a simplified setting that distills key facets of these methods and isolates the impact of linearly correlated noise.