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Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models.
Proper efficiency and the theory of vector maximization
Arthur M Geoffrion · 1968
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Microeconomic Theory
Andreu Mas-Colell, Michael Whinston, and Jerry Green · 1995
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Efficient projections onto the l1-ball for learning in high dimensions
John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra · 2008
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Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2011
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Nonlinear Multiobjective Optimization
Kaisa Miettinen · 2012
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UCI machine learning repository, 2013
M. Lichman · 2013
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konecný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik · 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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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel · 2016
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Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
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Robust optimization for non-convex objectives
Robert S. Chen, Brendan Lucier, Yaron Singer, and Vasilis Syrgkanis · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Reform of eu data protection rules 2018
European Commission · 2018
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A comparative study of fairness-enhancing interventions in machine learning
Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, and Derek Roth · 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, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, 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 · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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A snapshot of the frontiers of fairness in machine learning
Alexandra Chouldechova and Aaron Roth · 2020
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Distributionally robust federated averaging
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 2020
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Convergent algorithms for (relaxed) minimax fairness
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, and Aaron Roth · 2020
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Retiring adult: New datasets for fair machine learning
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt · 2021
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Robust fairness-aware learning under sample selection bias
Wei Du and Xintao Wu · 2021
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Fairfed: Enabling group fairness in federated learning, 2021
Yahya H. Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and Salman Avestimehr · 2021
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Improving fairness for data valuation in federated learning, 2021
Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu, and Yong Zhang · 2021
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FjORD: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horváth, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Donald Lane · 2021
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Wei Du, Depeng Xu, Xintao Wu, and Hanghang Tong · 2020
Cited alongside, same era.
Fedmgda+: Federated learning meets multi-objective optimization
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, and Yaoliang Yu · 2020
Cited alongside, same era.
SCAFFOLD: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2020
Cited alongside, same era.
Collaborative Fairness in Federated Learning
Lingjuan Lyu, Xinyi Xu, Qian Wang, and Han Yu · 2020
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Minimax pareto fairness: A multi objective perspective
Natalia Martinez, Martin Bertran, and Guillermo Sapiro · 2020
Cited alongside, same era.
Robust federated learning: The case of affine distribution shifts
Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, and Ali Jadbabaie · 2020
Cited alongside, same era.
Tilted empirical risk minimization
Tian Li, Ahmad Beirami, Maziar Sanjabi, and Virginia Smith · 2021
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FedBN: Federated learning on non-IID features via local batch normalization
Xiaoxiao Li, Meirui JIANG, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2021
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Fedprune: Towards inclusive federated learning, 2021
Muhammad Tahir Munir, Muhammad Mustansar Saeed, Mahad Ali, Zafar Ayyub Qazi, and Ihsan Ayyub Qazi · 2021
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Game of gradients: Mitigating irrelevant clients in federated learning
Lokesh Nagalapatti and Ramasuri Narayanam · 2021
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Communication-Efficient Agnostic Federated Averaging
Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, and Ananda Theertha Suresh · 2021
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Enforcing fairness in private federated learning via the modified method of differential multipliers, 2021
Borja Rodríguez-Gálvez, Filip Granqvist, Rogier van Dalen, and Matt Seigel · 2021
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Federated learning with fair averaging
Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Chenglu Wen, Cheng Wang, and Rongshan Yu · 2021
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GIFAIR-FL: an approach for group and individual fairness in federated learning
Xubo Yue, Maher Nouiehed, and Raed Al Kontar · 2021
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Improving fairness via federated learning, 2021
Yuchen Zeng, Hongxu Chen, and Kangwook Lee · 2021
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Unified group fairness on federated learning, 2021
Fengda Zhang, Kun Kuang, Yuxuan Liu, Chao Wu, Fei Wu, Jiaxun Lu, Yunfeng Shao, and Jun Xiao · 2021
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