Brendan McMahan

26 papers A* 7A 2Misc 1Journal 15Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	JAX-Privacy: A library for differentially private machine learning. CoRR Ryan McKenna, Galen Andrew, Borja Balle, Vadym Doroshenko, Arun Ganesh, Weiwei Kong, Alex Kurakin, Brendan McMahan, Mikhail Pravilov
2025	J	`jnl`	Hush! Protecting Secrets During Model Training: An Indistinguishability Approach. CoRR Arun Ganesh, Brendan McMahan, Milad Nasr, Thomas Steinke, Abhradeep Thakurta
2025	J	`jnl`	It's My Data Too: Private ML for Datasets with Multi-User Training Examples. CoRR Arun Ganesh, Ryan McKenna, Brendan McMahan, Adam Smith, Fan Wu
2025	J	`jnl`	On Design Principles for Private Adaptive Optimizers. CoRR Arun Ganesh, Brendan McMahan, Abhradeep Thakurta
2024	J	`jnl`	Confidential Federated Computations. CoRR 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
2023	J	`jnl`	Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan
2023	A*	`conf`	How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy. KDD Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang
2022	J	`jnl`	Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning. CoRR Zachary Charles, Kallista A. Bonawitz, Stanislav Chiknavaryan, Brendan McMahan, Blaise Agüera y Arcas
2022	J	`jnl`	Federated learning and privacy. Commun. ACM Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
2022	J	`jnl`	Private Online Prefix Sums via Optimal Matrix Factorizations. CoRR Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta
2021	A*	`conf`	Differentially Private Learning with Adaptive Clipping. NeurIPS Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy
2021	J	`jnl`	Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data. ACM Queue Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage
2021	A*	`conf`	Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
2021	J	`jnl`	Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
2020	A	`conf`	Federated Heavy Hitters Discovery with Differential Privacy. AISTATS Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li
2020	A*	`conf`	Privacy Amplification via Random Check-Ins. NeurIPS Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta
2019	J	`jnl`	Federated Heavy Hitters Discovery with Differential Privacy. CoRR Wennan Zhu, Peter Kairouz, Haicheng Sun, Brendan McMahan, Wei Li
2019	Misc	`conf`	Federated Learning with Autotuned Communication-Efficient Secure Aggregation. ACSSC Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser
2019	J	`jnl`	Federated Learning with Autotuned Communication-Efficient Secure Aggregation. CoRR Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser
2019	A*	`conf`	Semi-Cyclic Stochastic Gradient Descent. ICML Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar
2019	—	`conf`	Towards Federated Learning at Scale: System Design. SysML 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
2018	A*	`conf`	Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. NeurIPS Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro
2018	J	`jnl`	Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. CoRR Blake E. Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro
2018	A*	`conf`	cpSGD: Communication-efficient and differentially-private distributed SGD. NeurIPS Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan
2017	A	`conf`	Communication-Efficient Learning of Deep Networks from Decentralized Data. AISTATS Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas
2015	J	`jnl`	Federated Optimization: Distributed Optimization Beyond the Datacenter. CoRR Jakub Konecný, Brendan McMahan, Daniel Ramage