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Federated learning (FL) has emerged as an important machine learning paradigm where a global model is trained based on the private data from distributed clients.
Fair resource allocation in federated learning
Li, T.; Sanjabi, M.; Beirami, A.; and Smith, V. 2019 · 1905
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Advances and open problems in federated learning
Kairouz, P.; McMahan, H. B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A. N.; Bonawitz, K.; Charles, Z.; Cormode, G.; Cummings, R.; et al. 2019 · 1912
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Learning non-discriminatory predictors
Woodworth, B.; Gunasekar, S.; Ohannessian, M. I.; and Srebro, N. 2017 · 1953
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A method for solving the convex programming problem with convergence rate O (1/kˆ 2)
Nesterov, Y. E. 1983 · 1983
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Justice as fairness: A restatement
Rawls, J. 2001 · 2001
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Federated learning of a mixture of global and local models
Hanzely, F.; and Richtárik, P. 2020 · 2002
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Adaptive personalized federated learning
Deng, Y.; Kamani, M. M.; and Mahdavi, M. 2020a · 2003
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Beyond individual and group fairness
Awasthi, P.; Cortes, C.; Mansour, Y.; and Mohri, M. 2020 · 2008
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Distributionally robust optimization under moment uncertainty with application to data-driven problems
Delage, E.; and Ye, Y. 2010 · 2010
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Fairness through awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
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Learning fair representations
Zemel, R.; Wu, Y.; Swersky, K.; Pitassi, T.; and Dwork, C. 2013 · 2013
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Distributionally robust convex optimization
Wiesemann, W.; Kuhn, D.; and Sim, M. 2014 · 2014
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Big data’s disparate impact
Barocas, S.; and Selbst, A. D. 2016 · 2016
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
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Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
Namkoong, H.; and Duchi, J. C. 2016 · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A. 2017 · 2017
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UCI Machine Learning Repository
Dua, D.; and Graff, C. 2017 · 2017
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Variance-based regularization with convex objectives
Duchi, J.; and Namkoong, H. 2017 · 2017
Cited alongside, same era.
Convergence analysis of proximal gradient with momentum for nonconvex optimization
Li, Q.; Zhou, Y.; Liang, Y.; and Varshney, P. K. 2017 · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
Cited alongside, same era.
Distributed mean estimation with limited communication
Suresh, A. T.; Felix, X. Y.; Kumar, S.; and McMahan, H. B. 2017 · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
Cited alongside, same era.
Equity of attention: Amortizing individual fairness in rankings
Average individual fairness: Algorithms, generalization and experiments
Sharifi-Malvajerdi, S.; Kearns, M.; and Roth, A. 2019 · 2019
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A hybrid approach to privacy-preserving federated learning
Truex, S.; Baracaldo, N.; Anwar, A.; Steinke, T.; Ludwig, H.; Zhang, R.; and Zhou, Y. 2019 · 2019
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Federated machine learning: Concept and applications
Yang, Q.; Liu, Y.; Chen, T.; and Tong, Y. 2019 · 2019
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On the apparent conflict between individual and group fairness
Binns, R. 2020 · 2020
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Two simple ways to learn individual fairness metrics from data
Mukherjee, D.; Yurochkin, M.; Banerjee, M.; and Sun, Y. 2020 · 2020
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Distributionally Robust Neural Networks
Sagawa, S.; Koh, P. W.; Hashimoto, T. B.; and Liang, P. 2020 · 2020
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Biega, A. J.; Gummadi, K. P.; and Weikum, G. 2018 · 2018
Cited alongside, same era.
Decoupled classifiers for group-fair and efficient machine learning
Dwork, C.; Immorlica, N.; Kalai, A. T.; and Leiserson, M. 2018 · 2018
Cited alongside, same era.
Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations
Esfahani, P. M.; and Kuhn, D. 2018 · 2018
Cited alongside, same era.
Local convergence of the heavy-ball method and ipiano for non-convex optimization
Ochs, P. 2018 · 2018
Cited alongside, same era.
Certifying some distributional robustness with principled adversarial training
Sinha, A.; Namkoong, H.; Volpi, R.; and Duchi, J. 2018 · 2018
Cited alongside, same era.
Deep cocktail network: Multi-source unsupervised domain adaptation with category shift
Xu, R.; Chen, Z.; Zuo, W.; Yan, J.; and Lin, L. 2018 · 2018
Cited alongside, same era.
Federated learning with non-iid data
Zhao, Y.; Li, M.; Lai, L.; Suda, N.; Civin, D.; and Chandra, V. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Federated learning with only positive labels
Yu, F.; Rawat, A. S.; Menon, A.; and Kumar, S. 2020 · 2020
Later among the works it cites.
A learning-based incentive mechanism for federated learning
Zhan, Y.; Li, P.; Qu, Z.; Zeng, D.; and Guo, S. 2020 · 2020
Later among the works it cites.
Multi-source distilling domain adaptation
Zhao, S.; Wang, G.; Zhang, S.; Gu, Y.; Li, Y.; Song, Z.; Xu, P.; Hu, R.; Chai, H.; and Keutzer, K. 2020 · 2020
Later among the works it cites.
Fairness-aware Agnostic Federated Learning
Du, W.; Xu, D.; Wu, X.; and Tong, H. 2021 · 2021
Closest in time.
Learning models with uniform performance via distributionally robust optimization
Duchi, J. C.; and Namkoong, H. 2021 · 2021
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Ditto: Fair and robust federated learning through personalization
Li, T.; Hu, S.; Beirami, A.; and Smith, V. 2021 · 2021
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Stable Adversarial Learning under Distributional Shifts
Liu, J.; Shen, Z.; Cui, P.; Zhou, L.; Kuang, K.; Li, B.; and Lin, Y. 2021 · 2021
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A survey on bias and fairness in machine learning
Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; and Galstyan, A. 2021 · 2021
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Federated Learning with Fair Averaging
Wang, Z.; Fan, X.; Qi, J.; Wen, C.; Wang, C.; and Yu, R. 2021 · 2021
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Does distributionally robust supervised learning give robust classifiers?
Hu, W.; Niu, G.; Sato, I.; and Sugiyama, M. 2018 · 2037
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