Fetching the paper…
Reading the bibliography…
Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Privacy-preserving datamining on vertically partitioned databases
Cynthia Dwork and Kobbi Nissim · 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.
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, and Kunal Talwar · 2007
Earlier work this paper cites.
Accurate estimation of the degree distribution of private networks
Michael Hay, Chao Li, Gerome Miklau, and David Jensen · 2009
Earlier work this paper cites.
Multiparty differential privacy via aggregation of locally trained classifiers
Manas Pathak, Shantanu Rane, and Bhiksha Raj · 2010
Earlier work this paper cites.
Probabilistic inference and differential privacy
Oliver Williams and Frank McSherry · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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.
The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
Earlier work this paper cites.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
Differentially private learning of undirected graphical models using collective graphical models
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, and Gerome Miklau · 2017
Earlier work this paper cites.
Differentially private federated learning: A client level perspective
R. C. Geyer, T. Klein, and M. Nabi · 2017
Earlier work this paper cites.
Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
Earlier work this paper cites.
Less than a single pass: Stochastically controlled stochastic gradient
Lihua Lei and Michael Jordan · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
Cited alongside, same era.
Distributed mean estimation with limited communication
Ananda Theertha Suresh, Felix X Yu, Sanjiv Kumar, and H Brendan McMahan · 2017
Cited alongside, same era.
Differentially private empirical risk minimization revisited: Faster and more general
Di Wang, Minwei Ye, and Jinhui Xu · 2017
Cited alongside, same era.
Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
Later among the works it cites.
Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
Later among the works it cites.
Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
Later among the works it cites.
On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
Later among the works it cites.
Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 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…
cpSGD: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X 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.
Composable and versatile privacy via truncated cdp
Mark Bun, Cynthia Dwork, Guy N Rothblum, and Thomas Steinke · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Privacy amplification by iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta · 2018
Cited alongside, same era.
Rényi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
Later among the works it cites.
Revisiting graph neural networks: All we have is low-pass filters
Hoang Nt and Takanori Maehara · 2019
Later among the works it cites.
White-box vs black-box: Bayes optimal strategies for membership inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou · 2019
Later among the works it cites.
Efficient privacy-preserving stochastic nonconvex optimization
Lingxiao Wang, Bargav Jayaraman, David Evans, and Quanquan Gu · 2019
Later among the works it cites.
Subsampled rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
Later among the works it cites.
Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
Later among the works it cites.
An efficient federated learning scheme with differential privacy in mobile edge computing
Jiale Zhang, Junyu Wang, Yanchao Zhao, and Bing Chen · 2019
Later among the works it cites.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 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.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
Closest in time.
Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Closest in time.
Tighter theory for local sgd on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
Closest in time.
Improved protein structure prediction using potentials from deep learning
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, et al · 2020
Closest in time.
DP-LSSGD: A stochastic optimization method to lift the utility in privacy-preserving ERM
Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, and Stanley J. Osher · 2020
Closest in time.
Characterizing membership privacy in stochastic gradient langevin dynamics
Bingzhe Wu, Chaochao Chen, Shiwan Zhao, Cen Chen, Yuan Yao, Guangyu Sun, Li Wang, Xiaolu Zhang, and Jun Zhou · 2020
Closest in time.