Fetching the paper…
Reading the bibliography…
We consider training models on private data that are distributed across user devices.
“Towards Federated Learning at Scale: System Design”
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Overveldt, David Petrou, Daniel Ramage and Jason Roselander · 1902
Earlier work this paper cites.
“Randomized response: A survey technique for eliminating evasive answer bias”
Stanley Warner · 1965
Earlier work this paper cites.
“The Discrete Gaussian for Differential Privacy”
Cl“’ement Canonne, Gautam Kamath and Thomas Steinke · 2004
Earlier work this paper cites.
“Privacy preserving mining of association rules”
Alexandre Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal and Johannes Gehrke · 2004
Earlier work this paper cites.
“Our data, ourselves: Privacy via distributed noise generation”
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov and Moni Naor · 2006
Earlier work this paper cites.
“Calibrating noise to sensitivity in private data analysis”
Cynthia Dwork, Frank McSherry, Kobbi Nissim and Adam Smith · 2006
Earlier work this paper cites.
“Cryptography from anonymity”
Yuval Ishai, Eyal Kushilevitz, Rafail Ostrovsky and Amit Sahai · 2006
Earlier work this paper cites.
“How to generate random matrices from the classical compact groups”
Francesco Mezzadri · 2006
Earlier work this paper cites.
“Learning multiple layers of features from tiny images”
Alex Krizhevsky · 2009
Earlier work this paper cites.
“MNIST handwritten digit database”
Yann LeCun, Corinna Cortes and CJ Burges · 2010
Earlier work this paper cites.
“What Can We Learn Privately?”
Shiva Kasiviswanathan, Homin. Lee, Kobbi Nissim, Sofya Raskhodnikova and Adam Smith · 2011
Earlier work this paper cites.
“Local privacy and statistical minimax rates”
John Duchi, Michael Jordan and Martin Wainwright · 2013
Earlier work this paper cites.
“Secure multiparty aggregation with differential privacy: A comparative study”
Slawomir Goryczka, Li Xiong and Vaidy Sunderam · 2013
Earlier work this paper cites.
“Stochastic gradient descent with differentially private updates”
Shuang Song, Kamalika Chaudhuri and Anand Sarwate · 2013
Earlier work this paper cites.
“Private empirical risk minimization: Efficient algorithms and tight error bounds”
Raef Bassily, Adam Smith and Abhradeep Thakurta · 2014
Earlier work this paper cites.
“Distribution of scalar products of two random unit vectors in D D dimensions”, Cross Validated Stack Exchange, 2014
whuber · 2014
Earlier work this paper cites.
“Robust traceability from trace amounts”
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman and Salil Vadhan · 2015
Earlier work this paper cites.
“Deep learning with differential privacy”
Martin Abadi, Andy Chu, Ian Goodfellow, H McMahan, Ilya Mironov, Kunal Talwar and Li Zhang · 2016
Earlier work this paper cites.
“Practical secure aggregation for federated learning on user-held data”
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal and Karn Seth · 2016
Earlier work this paper cites.
“Concentrated differential privacy: Simplifications, extensions, and lower bounds”
Mark Bun and Thomas Steinke · 2016
Earlier work this paper cites.
“Concentrated differential privacy”
Cynthia Dwork and Guy Rothblum · 2016
Earlier work this paper cites.
“Discrete distribution estimation under local privacy”
Peter Kairouz, Keith Bonawitz and Daniel Ramage · 2016
Earlier work this paper cites.
“Prochlo: Strong Privacy for Analytics in the Crowd”
Andrea Bittau, “’Ulfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes and Bernhard Seefeld · 2017
Earlier work this paper cites.
“Practical secure aggregation for privacy-preserving machine learning”
K.. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal and Karn Seth · 2017
Earlier work this paper cites.
“EMNIST: Extending MNIST to handwritten letters”
Gregory Cohen, Saeed Afshar, Jonathan Tapson and Andre Van · 2017
Earlier work this paper cites.
“On the sub-Gaussianity of the Beta and Dirichlet distributions”
Olivier Marchal and Julyan Arbel · 2017
Cited alongside, same era.
“R’enyi differential privacy”
Ilya Mironov · 2017
Cited alongside, same era.
“Communication-Efficient Learning of Deep Networks from Decentralized Data”
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson and Blaise y Arcas · 2017
Cited alongside, same era.
“Distributed mean estimation with limited communication”
Ananda Suresh, X Felix, Sanjiv Kumar and H McMahan · 2017
Cited alongside, same era.
“Membership inference attacks against machine learning models”
Reza Shokri, Marco Stronati, Congzheng Song and Vitaly Shmatikov · 2017
Cited alongside, same era.
“Computational differential privacy from lattice-based cryptography”
Filipp Valovich and Francesco Alda · 2017
“Auditing data provenance in text-generation models”
Congzheng Song and Vitaly Shmatikov · 2019
Later among the works it cites.
“Overlearning reveals sensitive attributes”
Congzheng Song and Vitaly Shmatikov · 2019
Later among the works it cites.
“A hybrid approach to privacy-preserving federated learning”
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang and Yi Zhou · 2019
Later among the works it cites.
“Subsampled Renyi Differential Privacy and Analytical Moments Accountant”
Yu-Xiang Wang, Borja Balle and Shiva Kasiviswanathan · 2019
Later among the works it cites.
“Poission subsampled rényi differential privacy”
Yuqing Zhu and Yu-Xiang Wang · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
“cpSGD: Communication-efficient and differentially-private distributed SGD”
Naman Agarwal, Ananda Suresh, Felix Xinnan Yu, Sanjiv Kumar and Brendan McMahan · 2018
Cited alongside, same era.
“Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising”
Borja Balle and Yu-Xiang Wang · 2018
Cited alongside, same era.
“Leaf: A benchmark for federated settings”
Sebastian Caldas, Sai Meher Duddu, Peter Wu, Tian Li, Jakub Konecn“‘y, H McMahan, Virginia Smith and Ameet Talwalkar · 2018
Cited alongside, same era.
“Federated learning for mobile keyboard prediction”
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Francoise Beaufays, Sean Augenstein, Hubert Eichner, Chlo“’e Kiddon and Daniel Ramage · 2018
Cited alongside, same era.
“Learning Differentially Private Recurrent Language Models”
H McMahan, Daniel Ramage, Kunal Talwar and Li Zhang · 2018
Cited alongside, same era.
“Differentially private learning with adaptive clipping”
Galen Andrew, Om Thakkar, H McMahan and Swaroop Ramaswamy · 2019
Cited alongside, same era.
“A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences”
S. Asoodeh, J. Liao, F.. Calmon, O. Kosut and L. Sankar · 2020
Later among the works it cites.
“Hypothesis testing interpretations and renyi differential privacy”
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu and Tetsuya Sato · 2020
Later among the works it cites.
“Secure Single-Server Aggregation with (Poly)Logarithmic Overhead” https://eprint.iacr.org/2020/704 , Cryptology ePrint Archive, Report 2020/704, 2020
James Bell, K.. Bonawitz, Adrià Gascón, Tancrède Lepoint and Mariana Raykova · 2020
Later among the works it cites.
“Private Summation in the Multi-Message Shuffle Model”
Borja Balle, James Bell, Adri“‘a Gasc“’on and Kobbi Nissim · 2020
Later among the works it cites.
“Separating Local & Shuffled Differential Privacy via Histograms”
Victor Balcer and Albert Cheu · 2020
Later among the works it cites.
“Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs”
Antonious Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz and Ananda Suresh · 2020
Later among the works it cites.
“Pure Differentially Private Summation from Anonymous Messages”
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh and Ameya Velingker · 2020
Later among the works it cites.
“Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead”
Badih Ghazi, Ravi Kumar, Pasin Manurangsi and Rasmus Pagh · 2020
Later among the works it cites.
“Private Aggregation from Fewer Anonymous Messages”
Badih Ghazi, Pasin Manurangsi, Rasmus Pagh and Ameya Velingker · 2020
Later among the works it cites.
“Improved discrete Gaussian and subgaussian analysis for lattice cryptography”
Nicholas Genise, Daniele Micciancio, Chris Peikert and Michael Walter · 2020
Later among the works it cites.
“Adaptive federated optimization”
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecn“‘y, Sanjiv Kumar and H McMahan · 2020
Later among the works it cites.
“Differentially Private Learning Needs Better Features (or Much More Data)”
Florian Tram“‘er and Dan Boneh · 2020
Later among the works it cites.
“Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms”
Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing and Afshin Rostamizadeh · 2021
Closest in time.
“Connecting robust shuffle privacy and pan-privacy”
Victor Balcer, Albert Cheu, Matthew Joseph and Jieming Mao · 2021
Closest in time.
“On the Power of Multiple Anonymous Messages” To appear
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh and Ameya Velingker · 2021
Closest in time.
“Practical and Private (Deep) Learning without Sampling or Shuffling”
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta and Zheng Xu · 2021
Closest in time.
“Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning”
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot and Nicholas Carlini · 2021
Closest in time.
“Fast Fourier Transform” https://en.wikipedia.org/wiki/Fast_Fourier_transform#Other_FFT_algorithms , 2021
Wikipedia · 2021
Closest in time.
“D2P-Fed: Differentially Private Federated Learning With Efficient Communication”
Lun Wang, Ruoxi Jia and Dawn Song · 2021
Closest in time.