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Federated learning enables the development of a machine learning model among collaborating agents without requiring them to share their underlying data.
How to share a secret
Adi Shamir · 1979
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How to generate and exchange secrets (extended abstract)
Andrew Chi-Chih Yao · 1986
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Multiparty unconditionally secure protocols (abstract)
David Chaum, Claude Crépeau, and Ivan Damgård · 1987
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Completeness theorems for non-cryptographic fault-tolerant distributed computation (extended abstract)
Michael Ben-Or, Shafi Goldwasser, and Avi Wigderson · 1988
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Secure multi-party computation
Oded Goldreich · 1998
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Results of the kdd’99 classifier learning
Charles Elkan · 2000
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Stuffing the ballot box: fraud, electoral reform, and democratization in Costa Rica
Fabrice E Lehoucq and Iván Molina · 2002
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Hipaa regulations-a new era of medical-record privacy?
George J Annas et al · 2003
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Cryptanalysis and improvement of an elliptic curve diffie-hellman key agreement protocol
Shengbao Wang, Zhenfu Cao, Maurizio Adriano Strangio, and Lihua Wang · 2008
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2009
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Differential privacy for statistics: What we know and what we want to learn
Cynthia Dwork and Adam Smith · 2010
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Ipfs-content addressed, versioned, p2p file system
Juan Benet · 2014
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Ethereum: A secure decentralised generalised transaction ledger
Gavin Wood · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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A methodology for formalizing model-inversion attacks
Xi Wu, Matthew Fredrikson, Somesh Jha, and Jeffrey F Naughton · 2016
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Achieving differential privacy in secure multiparty data aggregation protocols on star networks
Vincent Bindschaedler, Shantanu Rane, Alejandro E Brito, Vanishree Rao, and Ersin Uzun · 2017
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A machine learning-based framework to identify type 2 diabetes through electronic health records
Tao Zheng, Wei Xie, Liling Xu, Xiaoying He, Ya Zhang, Mingrong You, Gong Yang, and You Chen · 2017
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Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers · 2018
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When machine learning meets blockchain: A decentralized, privacy-preserving and secure design
Xuhui Chen, Jinlong Ji, Changqing Luo, Weixian Liao, and Pan Li · 2018
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A Besir Kurtulmus and Kenny Daniel · 2018
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Poster: A reliable and accountable privacy-preserving federated learning framework using the blockchain
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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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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Official go implementation of the ethereum protocol
Go Ethereum · 2017
Cited alongside, same era.
Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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Federated learning: Collaborative machine learning without centralized training data
Brendan McMahan and Daniel Ramage · 2017
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
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Comparing blockchain and cloud services for business process execution
Paul Rimba, An Binh Tran, Ingo Weber, Mark Staples, Alexander Ponomarev, and Xiwei Xu · 2017
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Sana Awan, Fengjun Li, Bo Luo, and Mei Liu · 2019
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Decentralized and collaborative ai on blockchain
Justin D Harris and Bo Waggoner · 2019
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Efficient privacy-preserving machine learning for blockchain network
Hyunil Kim, Seung-Hyun Kim, Jung Yeon Hwang, and Changho Seo · 2019
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Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets
Liping Li, Wei Xu, Tianyi Chen, Georgios B Giannakis, and Qing Ling · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
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Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks
Zhaoxian Wu, Qing Ling, Tianyi Chen, and Georgios B Giannakis · 2019
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