Brendan McMahan

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 22

Top Topics

Matrix Factorization

Privacy Preservation

Differentially Private

Top Venues

Publications

Hubert Eichner, Daniel Ramage, Kallista A. Bonawitz, Dzmitry Huba, Tiziano Santoro, Brett McLarnon, Timon Van Overveldt, Nova Fallen, Peter Kairouz, Albert Cheu, Katharine Daly, Adrià Gascón, Marco Gruteser, Brendan McMahan
Confidential Federated Computations. CoRR (2024)
Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR (2023)
Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang
How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy. KDD (2023)
Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta
Private Online Prefix Sums via Optimal Matrix Factorizations. CoRR (2022)
Zachary Charles, Kallista A. Bonawitz, Stanislav Chiknavaryan, Brendan McMahan, Blaise Agüera y Arcas
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning. CoRR (2022)
Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
Federated learning and privacy. Commun. ACM 65 (4) (2022)
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR (2021)
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML (2021)
Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data. ACM Queue 19 (5) (2021)
Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy
Differentially Private Learning with Adaptive Clipping. NeurIPS (2021)
Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta
Privacy Amplification via Random Check-Ins. NeurIPS (2020)
Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li
Federated Heavy Hitters Discovery with Differential Privacy. AISTATS (2020)
Wennan Zhu, Peter Kairouz, Haicheng Sun, Brendan McMahan, Wei Li
Federated Heavy Hitters Discovery with Differential Privacy. CoRR (2019)
Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser
Federated Learning with Autotuned Communication-Efficient Secure Aggregation. CoRR (2019)
Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander
Towards Federated Learning at Scale: System Design. MLSys (2019)
Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar
Semi-Cyclic Stochastic Gradient Descent. ICML (2019)
Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser
Federated Learning with Autotuned Communication-Efficient Secure Aggregation. ACSSC (2019)
Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. NeurIPS (2018)
Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan
cpSGD: Communication-efficient and differentially-private distributed SGD. NeurIPS (2018)
Blake E. Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. CoRR (2018)
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas
Communication-Efficient Learning of Deep Networks from Decentralized Data. AISTATS (2017)
Jakub Konecný, Brendan McMahan, Daniel Ramage
Federated Optimization: Distributed Optimization Beyond the Datacenter. CoRR (2015)