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Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Gossip-based computation of aggregate information
David Kempe, Alin Dobra, and Johannes Gehrke · 2003
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Open MPI: A flexible high performance MPI
Richard L Graham, Timothy S Woodall, and Jeffrey M Squyres · 2005
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Non-negative Matrices and Markov Chains
E. Seneta · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Stochastic gradient-push for strongly convex functions on time-varying directed graphs
Angelia Nedić and Alex Olshevsky · 2016
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Torchvision: Pytorch’s computer vision library
TorchVision · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection
Konstantin Pogorelov, Kristin Ranheim Randel, Carsten Griwodz, Sigrun Losada Eskeland, Thomas de Lange, Dag Johansen, Concetto Spampinato, Duc-Tien Dang-Nguyen, Mathias Lux, Peter Thelin Schmidt, et al · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers · 2018
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From detection of individual metastases to classification of lymph node status at the patient level: The camelyon17 challenge
Péter Bándi, Oscar Geessink, Quirine Manson, Marcory Van Dijk, Maschenka Balkenhol, Meyke Hermsen, Babak Ehteshami Bejnordi, Byungjae Lee, Kyunghyun Paeng, Aoxiao Zhong, Quanzheng Li, Farhad Ghazvinian Zanjani, Svitlana Zinger, Keisuke Fukuta, Daisuke Komura, Vlado Ovtcharov, Shenghua Cheng, Shaoqun Zeng, Jeppe Thagaard, Anders B. Dahl, Huangjing Lin, Hao Chen, Ludwig Jacobsson, Martin Hedlund, Melih Çetin, Eren Halıcı, Hunter Jackson, Richard Chen, Fabian Both, Jörg Franke, Heidi Küsters-Vandevelde, Willem Vreuls, Peter Bult, Bram van Ginneken, Jeroen van der Laak, and Geert Litjens · 2018
Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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Siloed federated learning for multi-centric histopathology datasets
Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier, and Eric W Tramel · 2020
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Hypothesis testing interpretations and Renyi differential privacy
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato · 2020
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Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers—From the Radiology editorial board
David A. Bluemke, Linda Moy, Miriam A. Bredella, Birgit B. Ertl-Wagner, Kathryn J. Fowler, Vicky J. Goh, Elkan F. Halpern, Christopher P. Hess, Mark L. Schiebler, and Clifford R. Weiss · 2020
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Auditing differentially private machine learning: How private is private SGD?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
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Distributed deep learning networks among institutions for medical imaging
Ken Chang, Niranjan Balachandar, Carson Lam, Darvin Yi, James Brown, Andrew Beers, Bruce Rosen, Daniel L Rubin, and Jayashree Kalpathy-Cramer · 2018
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Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
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Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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Network topology and communication-computation tradeoffs in decentralized optimization
Angelia Nedić, Alex Olshevsky, and Michael G. Rabbat · 2018
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Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction
Seong Ho Park and Kyunghwa Han · 2018
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Artificial intelligence and digital pathology: Challenges and opportunities
Hamid Reza Tizhoosh and Liron Pantanowitz · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Deep mutual learning
Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu · 2018
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Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
Xiaoxiao Li, Yufeng Gu, Nicha Dvornek, Lawrence H. Staib, Pamela Ventola, and James S. Duncan · 2020
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
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Ensuring machine learning for healthcare works for all
Liam G McCoy, John D Banja, Marzyeh Ghassemi, and Leo Anthony Celi · 2020
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Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Kun Kuang, Fei Wu, and Chao Wu · 2020
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Decentralized federated learning preserves model and data privacy
Thorsten Wittkopp and Alexander Acker · 2020
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Problems in the deployment of machine-learned models in health care
Joseph Paul Cohen, Tianshi Cao, Joseph D Viviano, Chin-Wei Huang, Michael Fralick, Marzyeh Ghassemi, Muhammad Mamdani, Russell Greiner, and Yoshua Bengio · 2021
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Personalized cross-silo federated learning on non-IID data
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Style normalization in histology with federated learning
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Decentralized federated learning via mutual knowledge transfer
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Federated learning for computational pathology on gigapixel whole slide images
Ming Y. Lu, Richard J. Chen, Dehan Kong, Jana Lipkova, Rajendra Singh, Drew F.K. Williamson, Tiffany Y. Chen, and Faisal Mahmood · 2021
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Adaptive distillation for decentralized learning from heterogeneous clients
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Adversary instantiation: Lower bounds for differentially private machine learning
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Federated model distillation with noise-free differential privacy
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Mitigating bias in machine learning for medicine
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Swarm learning for decentralized and confidential clinical machine learning
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Flop: Federated learning on medical datasets using partial networks
Qian Yang, Jianyi Zhang, Weituo Hao, Gregory P Spell, and Lawrence Carin · 2021
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Federated learning and differential privacy for medical image analysis
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