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Federated learning (FL) enables clients to collaborate with a server to train a machine learning model.
New directions in cryptography
W. Diffie and M. Hellman · 1976
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Adi Shamir · 1979
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Probabilistic algorithms for sparse polynomials
Richard Zippel · 1979
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J. T. Schwartz · 1980
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Theory and practice of error control codes
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Asynchronous consensus and broadcast protocols
Gabriel Bracha and Sam Toueg · 1985
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Paul Feldman · 1987
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Donald Beaver · 1992
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Publicly verifiable secret sharing
Markus Stadler · 1996
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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A simple publicly verifiable secret sharing scheme and its application to electronic voting
Berry Schoenmakers · 1999
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A New Algorithm for Decoding Reed-Solomon Codes
Shuhong Gao · 2003
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The guruswami–sudan decoding algorithm for reed–solomon codes, 2003
R. J. McEliece · 2003
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Error control coding: fundamentals and applications
Shu Lin and Daniel J. Costello · 2004
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Approximate nearest neighbors and the fast johnson-lindenstrauss transform
Nir Ailon and Bernard Chazelle · 2006
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Casting out demons: Sanitizing training data for anomaly sensors
Gabriela F. Cretu, Angelos Stavrou, Michael E. Locasto, Salvatore J. Stolfo, and Angelos D. Keromytis · 2008
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Efficient data structures for tamper-evident logging
Scott A. Crosby and Dan S. Wallach · 2009
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I have a dream! differentially private smart metering
Gergely Ács and Claude Castelluccia · 2011
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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(leveled) fully homomorphic encryption without bootstrapping
Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan · 2012
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Modern Computer Algebra
Joachim von zur Gathen and Jrgen Gerhard · 2013
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Introduction to Modern Cryptography, Second Edition
Jonathan Katz and Yehuda Lindell · 2014
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Using machine teaching to identify optimal training-set attacks on machine learners
Shike Mei and Xiaojin Zhu · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Auror: Defending against poisoning attacks in collaborative deep learning systems
Shiqi Shen, Shruti Tople, and Prateek Saxena · 2016
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Byzantine-tolerant machine learning
P. Blanchard, E. M. E. Mhamdi, R. Guerraoui, and J. Stainer · 2017
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Prio: Private, robust, and scalable computation of aggregate statistics
Henry Corrigan-Gibbs and Dan Boneh · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Cited alongside, same era.
Threshold fully homomorphic encryption
Aayush Jain, Peter M. R. Rasmussen, and Amit Sahai · 2017
Cited alongside, same era.
Zeno++: robust asynchronous SGD with arbitrary number of byzantine workers
Cong Xie · 2019
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Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta · 2019
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Private query release assisted by public data
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, and Zhiwei Steven Wu · 2020
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The power of synergy in differential privacy: Combining a small curator with local randomizers
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Federated learning: Collaborative machine learning without centralized training data, 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.
Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W. Koh, and Percy S. Liang · 2017
Cited alongside, same era.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2018
Cited alongside, same era.
Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John C. Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan M. Rogers · 2018
Cited alongside, same era.
Threshold cryptosystems from threshold fully homomorphic encryption
Dan Boneh, Rosario Gennaro, Steven Goldfeder, Aayush Jain, Sam Kim, Peter M. R. Rasmussen, and Amit Sahai · 2018
Cited alongside, same era.
Draco: Byzantine-resilient distributed training via redundant gradients
Lingjiao Chen, Hongyi Wang, Zachary Charles, and Dimitris Papailiopoulos · 2018
Cited alongside, same era.
Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, and Uri Stemmer · 2020
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Secure single-server aggregation with (poly)logarithmic overhead
James Henry Bell, Kallista A. Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova · 2020
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Local model poisoning attacks to byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2020
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Secure byzantine-robust machine learning, 2020
Lie He, Sai Praneeth Karimireddy, and Martin Jaggi · 2020
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Learning to detect malicious clients for robust federated learning
Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen · 2020
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Justinian’s GAAvernor: Robust distributed learning with gradient aggregation agent
Xudong Pan, Mi Zhang, Duocai Wu, Qifan Xiao, Shouling Ji, and Zhemin Yang · 2020
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Certified robustness to label-flipping attacks via randomized smoothing
Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, and J. Zico Kolter · 2020
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Byzantine-resilient secure federated learning
Jinhyun So, Basak Guler, and A. Salman Avestimehr · 2020
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On certifying robustness against backdoor attacks via randomized smoothing, 2020
Binghui Wang, Xiaoyu Cao, Jinyuan jia, and Neil Zhenqiang Gong · 2020
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Dba: Distributed backdoor attacks against federated learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2020
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Zeno++: Robust fully asynchronous SGD
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta · 2020
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Rofl: Attestable robustness for secure federated learning
Lukas Burkhalter, Hidde Lycklama, Alexander Viand, Nicolas Küchler, and Anwar Hithnawi · 2021
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Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong · 2021
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Intrinsic certified robustness of bagging against data poisoning attacks
Jinyuan Jia, Xiaoyu Cao, and Neil Zhenqiang Gong · 2021
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The distributed discrete gaussian mechanism for federated learning with secure aggregation
Peter Kairouz, Ziyu Liu, and Thomas Steinke · 2021
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Leveraging public data for practical private query release, 2021
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, and Zhiwei Steven Wu · 2021
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Local and central differential privacy for robustness and privacy in federated learning, 2021
Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro · 2021
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Flguard: Secure and private federated learning, 2021
Thien Duc Nguyen, Phillip Rieger, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Ahmad-Reza Sadeghi, Thomas Schneider, and Shaza Zeitouni · 2021
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Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning
Virat Shejwalkar and Amir Houmansadr · 2021
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Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning
Virat Shejwalkar and Amir Houmansadr · 2021
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Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning, 2021
Jinhyun So, Basak Guler, and A. Salman Avestimehr · 2021
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Crfl: Certifiably robust federated learning against backdoor attacks
Chulin Xie, Minghao Chen, Pin-Yu Chen, and Bo Li · 2021
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See through gradients: Image batch recovery via gradinversion
Hongxu Yin, Arun Mallya, Arash Vahdat, José Manuel Álvarez, Jan Kautz, and Pavlo Molchanov · 2021
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Provable adversarial robustness for fractional lp threat models
Alexander Levine and Soheil Feizi · 2022
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Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification
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