Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary von McMahan, H. Brendan, Blum, Avrim

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How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy von Ponomareva, Natalia, Hazimeh, Hussein, Kurakin, Alex, Xu, Zheng, Denison, Carson, McMahan, H. Brendan, Vassilvitskii, Sergei, Chien, Steve, Thakurta, Abhradeep Guha

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How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy von Ponomareva, Natalia, Hazimeh, Hussein, Kurakin, Alex, Xu, Zheng, Denison, Carson, McMahan, H Brendan, Vassilvitskii, Sergei, Chien, Steve, Thakurta, Abhradeep

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Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy von Koloskova, Anastasia, McKenna, Ryan, Charles, Zachary, Rush, Keith, McMahan, Brendan

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Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning von Charles, Zachary, Bonawitz, Kallista, Chiknavaryan, Stanislav, McMahan, Brendan, Arcas, Blaise Agüera y

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A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs von McMahan, H. Brendan, Xu, Zheng, Zhang, Yanxiang

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Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams von Denisov, Sergey, McMahan, Brendan, Rush, Keith, Smith, Adam, Thakurta, Abhradeep Guha

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Deep Learning with Differential Privacy von Abadi, Martín, Chu, Andy, Goodfellow, Ian, McMahan, H Brendan, Mironov, Ilya, Talwar, Kunal, Zhang, Li

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Federated Learning with Autotuned Communication-Efficient Secure Aggregation von Bonawitz, Keith, Salehi, Fariborz, Konečný, Jakub, McMahan, Brendan, Gruteser, Marco

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Confidential Federated Computations von Eichner, Hubert, Ramage, Daniel, Bonawitz, Kallista, Huba, Dzmitry, Santoro, Tiziano, McLarnon, Brett, Van Overveldt, Timon, Fallen, Nova, Kairouz, Peter, Cheu, Albert, Daly, Katharine, Gascon, Adria, Gruteser, Marco, McMahan, Brendan

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Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy von Koloskova, Anastasia, McKenna, Ryan, Zachary, Charles, Rush, Keith, McMahan, Brendan

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Practical and Private (Deep) Learning without Sampling or Shuffling von Kairouz, Peter, McMahan, Brendan, Song, Shuang, Thakkar, Om, Thakurta, Abhradeep, Xu, Zheng

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An Empirical Evaluation of Federated Contextual Bandit Algorithms von Agarwal, Alekh, McMahan, H. Brendan, Xu, Zheng

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Federated Heavy Hitters Discovery with Differential Privacy von Zhu, Wennan, Kairouz, Peter, McMahan, Brendan, Sun, Haicheng, Li, Wei

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Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization von Woodworth, Blake, Wang, Jialei, Smith, Adam, McMahan, Brendan, Srebro, Nathan

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SERVER EFFICIENT ENHANCEMENT OF PRIVACY IN FEDERATED LEARNING von Thakkar, Om, Kairouz, Peter, de Balle Pigem, Borja, Thakurta, Abhradeep Guha, McMahan, Brendan

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Federated Optimization:Distributed Optimization Beyond the Datacenter von Konečný, Jakub, McMahan, Brendan, Ramage, Daniel

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Federated Learning in Practice: Reflections and Projections von Daly, Katharine, Eichner, Hubert, Kairouz, Peter, McMahan, H. Brendan, Ramage, Daniel, Xu, Zheng

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SERVER EFFICIENT ENHANCEMENT OF PRIVACY IN FEDERATED LEARNING von THAKURTA, Abhradeep Guha, KAIROUZ, Peter, THAKKAR, Om, DE BALLE PIGEM, Borja, MCMAHAN, Brendan

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Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams von Denisov, Sergey, McMahan, Brendan, Rush, Keith, Smith, Adam, Abhradeep Guha Thakurta

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