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Federated learning has quickly gained popularity with its promises of increased user privacy and efficiency.
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 1912
Earlier work this paper cites.
The statistics of natural images
Daniel L. Ruderman · 1994
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Statistics of natural images and models
Jinggang Huang and David Mumford · 1999
Earlier work this paper cites.
A mathematical analysis of the DCT coefficient distributions for images
E.Y. Lam and J.W. Goodman · 2000
Earlier work this paper cites.
iDLG: Improved Deep Leakage from Gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2001
Earlier work this paper cites.
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
Earlier work this paper cites.
Foundations of Cryptography: Volume 2, Basic Applications
Oded Goldreich · 2009
Earlier work this paper cites.
Theory-Oriented Deep Leakage from Gradients via Linear Equation Solver
Xudong Pan, Mi Zhang, Yifan Yan, Jiaming Zhu, and Min Yang · 2010
Earlier work this paper cites.
Minimal conditions analysis of gradient-based reconstruction in Federated Learning
Jia Qian, Hiba Nassar, and Lars Kai Hansen · 2010
Earlier work this paper cites.
R-GAP: Recursive Gradient Attack on Privacy
Junyi Zhu and Matthew Blaschko · 2010
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth · 2013
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Federated Optimization:Distributed Optimization Beyond the Datacenter
Jakub Konečný, Brendan McMahan, and Daniel Ramage · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Privacy-Preserving Deep Learning
Reza Shokri and Vitaly Shmatikov · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Pointer Sentinel Mixture Models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Practical Secure Aggregation for Privacy Preserving Machine Learning
Kallista Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Cited alongside, same era.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 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.
Announcing 4 New Libraries for Federated Learning on Web and Mobile Devices , 2020
Patrick Cason · 2020
Later among the works it cites.
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chang Liu, Chee Seng Chan, and Qiang Yang · 2020
Later among the works it cites.
Inverting Gradients - How easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
Later among the works it cites.
Truth or backpropaganda? An empirical investigation of deep learning theory
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Federated Learning: Collaborative Machine Learning without Centralized Training Data, April 2017
Brendan McMahan and Daniel Ramage · 2017
Cited alongside, same era.
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 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.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2018
Cited alongside, same era.
Algorithms that remember: Model inversion attacks and data protection law
Michael Veale, Reuben Binns, and Lilian Edwards · 2018
Cited alongside, same era.
Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, and Tom Goldstein · 2020
Later among the works it cites.
What can we learn from gradients?
Jia Qian and Lars Kai Hansen · 2020
Later among the works it cites.
When the curious abandon honesty: Federated learning is not private
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot · 2021
Closest in time.
Gradient Inversion with Generative Image Prior
Jinwoo Jeon, Jaechang Kim, Kangwook Lee, Sewoong Oh, and Jungseul Ok · 2021
Closest in time.
Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, and Tom Goldstein · 2021
Closest in time.
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective
Jingwei Sun, Ang Li, Binghui Wang, Huanrui Yang, Hai Li, and Yiran Chen · 2021
Closest in time.
User Label Leakage from Gradients in Federated Learning
Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, and Max Mühlhäuser · 2021
Closest in time.
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, and Wennan Zhu · 2021
Closest in time.
See Through Gradients: Image Batch Recovery via GradInversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M. Alvarez, Jan Kautz, and Pavlo Molchanov · 2021
Closest in time.