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The idea of federated learning is to collaboratively train a neural network on a server.
On the limited memory BFGS method for large scale optimization
Dong C. Liu and Jorge Nocedal · 1989
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Nonlinear total variation based noise removal algorithms
Leonid I. Rudin, Stanley Osher, and Emad Fatemi · 1992
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Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
E. J. Candes, J. Romberg, and T. Tao · 2006
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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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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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Federated Optimization:Distributed Optimization Beyond the Datacenter
Jakub Konečný, Brendan McMahan, and Daniel Ramage · 2015
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Privacy-Preserving Deep Learning
Reza Shokri and Vitaly Shmatikov · 2015
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Generating Images with Perceptual Similarity Metrics based on Deep Networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Inverting Visual Representations With Convolutional Networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – A real life proof of concept
Arthur Jochems, Timo M. Deist, Johan van Soest, Michael Eble, Paul Bulens, Philippe Coucke, Wim Dries, Philippe Lambin, and Andre Dekker · 2016
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Visualizing Deep Convolutional Neural Networks Using Natural Pre-images
Aravindh Mahendran and Andrea Vedaldi · 2016
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Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, and Elliot Holtham · 2017
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Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries
Arthur Jochems, Timo M. Deist, Issam El Naqa, Marc Kessler, Chuck Mayo, Jackson Reeves, Shruti Jolly, Martha Matuszak, Randall Ten Haken, Johan van Soest, Cary Oberije, Corinne Faivre-Finn, Gareth Price, Dirk de Ruysscher, Philippe Lambin, and Andre Dekker · 2017
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Understanding Black-box Predictions via Influence Functions
Pang Wei Koh and Percy Liang · 2017
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Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Algorithms that remember: Model inversion attacks and data protection law
Michael Veale, Reuben Binns, and Lilian Edwards · 2018
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Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi · 2018
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Towards Federated Learning at Scale: System Design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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Input Similarity from the Neural Network Perspective
Guillaume Charpiat, Nicolas Girard, Loris Felardos, and Yuliya Tarabalka · 2019
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Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory
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H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
Cited alongside, same era.
Privacy-Preserving Deep Learning: Revisited and Enhanced
Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai · 2017
Cited alongside, same era.
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai · 2017
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Modern regularization methods for inverse problems
Martin Benning and Martin Burger · 2018
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Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov · 2018
Cited alongside, same era.
I-RevNet: Deep Invertible Networks
Jörn-Henrik Jacobsen, Arnold Smeulders, and Edouard Oyallon · 2018
Cited alongside, same era.
Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, and Tom Goldstein · 2019
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Evaluating Differentially Private Machine Learning in Practice
Bargav Jayaraman and David Evans · 2019
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Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Federated Machine Learning: Concept and Applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2019
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Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Adaptive Federated Optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H. Brendan McMahan · 2020
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iDLG: Improved Deep Leakage from Gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
Closest in time.