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As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored.
Three naive bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
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Data preprocessing techniques for classification without discrimination
F. Kamiran and T Calders · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Decision theory for discrimination-aware classification
Faisal Kamiran, Asim Karim, and Xiangliang Zhang · 2012
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Fairness-aware classifier with prejudice remover regularizer
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2012
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Discrimination in online ad delivery
Latanya Sweeney · 2013
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Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2015
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Big data can be used to violate civil rights laws, and the ftc agrees
E. Bhandari · 2016
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Satisfying real-world goals with dataset constraints
Gabriel Goh, Andrew Cotter, Maya Gupta, and Michael P Friedlander · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
Cited alongside, same era.
Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, Rachid Guerraoui, Julien Stainer, et al · 2017
Cited alongside, same era.
On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
Cited alongside, same era.
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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Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Towards federated graph learning for collaborative financial crimes detection
Toyotaro Suzumura, Yi Zhou, Natahalie Baracaldo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Yuji Watanabe, Daniel Klyashtorny Pablo Loyola, Heiko Ludwig, and Kumar Bhaskaran · 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, and Rui Zhang · 2019
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Hybridalpha: An efficient approach for privacy-preserving federated learning
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Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2018
Cited alongside, same era.
Bias detectives: the researchers striving to make algorithms fair
Rachel Courtland · 2018
Cited alongside, same era.
Ai is convicting criminals and determining jail time, but is it fair?
Vyacheslav Polonski · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett · 2018
Cited alongside, same era.
There’s software used across the country to predict future criminals. and its biased against blacks
J. Angwin, J. Larson, S. Mattu, and L. Mirchner · 2019
Cited alongside, same era.
Diffprivlib: The ibm differential privacy library
Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher · 2019
Cited alongside, same era.
Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid
R. Kohavi
Cited in the paper.
Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, and Heiko Ludwig · 2019
Later among the works it cites.
Tifl: A tier-based federated learning system
Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, and Yue Cheng · 2020
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Federated visual classification with real-world data distribution
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2020
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Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
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Ibm federated learning: an enterprise framework white paper v0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Whitherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Lagunaand Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, and Annie Abay · 2020
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Nvidia clara, 2020
NVIDIA · 2020
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Algorithms promised efficiency. but they’ve worsened inequality
Zamira Rahim · 2020
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