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Federated learning (FL) is getting increased attention for processing sensitive, distributed datasets common to domains such as healthcare.
On the fréchet distance of a set of curves
Adrian Dumitrescu and Günter Rote · 2004
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Adam: A method for stochastic optimization, 2014
Diederik P. Kingma and Jimmy Ba · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 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 learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
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Communication-efficient learning of deep networks from decentralized data
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, Mar. 2017
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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Netron, Visualizer for neural network, deep learning, and machine learning models, Dec. 2017
Lutz Roeder · 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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Understanding disentangling in
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
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LEAF: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Differentially Private Data Generative Models
Qingrong Chen, Chong Xiang, Minhui Xue, Bo Li, Nikita Borisov, and Dali Kaafar · 2018
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Differentially private federated learning: A client level perspective, 2018
Robin C. Geyer, Tassilo Klein, and Moin Nabi · 2018
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Learning Differentially Private Recurrent Language Models, Feb. 2018
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2018
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Scalable Private Learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson, and Google Brain · 2018
Cited alongside, same era.
Differentially Private Generative Adversarial Network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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Differentially Private Variational Autoencoders with Term-wise Gradient Aggregation, June 2020
Tsubasa Takahashi, Shun Takagi, Hajime Ono, and Tatsuya Komatsu · 2020
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Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning, Dec. 2020
Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, and Rachel Cummings · 2020
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Differentially Private Synthetic Medical Data Generation using Convolutional GANs
Amirsina Torfi, Edward A Fox, and Chandan K Reddy · 2020
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DP-CGAN : Differentially Private Synthetic Data and Label Generation
Reihaneh Torkzadehmahani, Peter Kairouz, Google Ai, and Benedict Paten · 2020
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Cited alongside, same era.
Differentially Private Mixture of Generative Neural Networks
Gergely Acs, Luca Melis, Claude Castelluccia, and Emiliano De Cristofaro · 2019
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing
Brett K. Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P. Bhavnani, James Brian Byrd, and Casey S. Greene · 2019
Cited alongside, same era.
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Cited alongside, same era.
Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data, Mar. 2019
Lorenzo Frigerio, Anderson Santana de Oliveira, Laurent Gomez, and Patrick Duverger · 2019
Cited alongside, same era.
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees
James Jordon and Jinsung Yoon · 2019
Cited alongside, same era.
Federated Generative Privacy
Aleksei Triastcyn and Boi Faltings · 2020
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Private FL-GAN: Differential Privacy Synthetic Data Generation Based on Federated Learning
Bangzhou Xin, Wei Yang, Yangyang Geng, Sheng Chen, Shaowei Wang, and Liusheng Huang · 2020
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Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, and Karsten Kreis · 2021
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GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Dingfan Chen, Tribhuvanesh Orekondy, and Mario Fritz · 2021
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Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder
Tal Daniel and Aviv Tamar · 2021
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Differentially Private Data Synthesis Using Variational Autoencoders
Margaritis Georgios · 2021
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DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation
Frederik Harder, Kamil Adamczewski, and Mijung Park · 2021
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A Generative Federated Learning Framework for Differential Privacy
Eugenio Lomurno, Leonardo Di Perna, Lorenzo Cazzella, Stefano Samele, and Matteo Matteucci · 2021
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G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators, Dec. 2021
Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, and Bo Li · 2021
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DPD-InfoGAN: Differentially Private Distributed InfoGAN
Vaikkunth Mugunthan, Vignesh Gokul, and Shlomo Dubnov · 2021
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FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia
Longling Zhang, Bochen Shen, Ahmed Barnawi, Shan Xi, Neeraj Kumar, Yi Wu, and Boshen Shen · 2021
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DP$2̂$-VAE: Differentially Private Pre-trained Variational Autoencoders
Dihong Jiang, Guojun Zhang, Mahdi Karami, Xi Chen, Yunfeng Shao, and Yaoliang Yu · 2022
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Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
Xue Jiang, Xuebing Zhou, and Jens Grossklags · 2022
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Defending against reconstruction attacks through differentially private federated learning for classification of heterogeneous chest x-ray data
Joceline Ziegler, Bjarne Pfitzner, Heinrich Schulz, Axel Saalbach, and Bert Arnrich · 2022
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