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Fair prediction across protected groups is an important constraint for many federated learning applications.
Fair resource allocation in federated learning
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A decision-theoretic generalization of on-line learning and an application to boosting
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Machine bias
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Compas risk scales: Demonstrating accuracy equity and predictive parity
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Equality of opportunity in supervised learning
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Optimized pre-processing for discrimination prevention
F. Calmon, D. Wei, B. Vinzamuri, K. Natesan Ramamurthy, and K. R. Varshney · 2017
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Learning non-discriminatory predictors
B. Woodworth, S. Gunasekar, M. I. Ohannessian, and N. Srebro · 2017
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A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach · 2018
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Leaf: A benchmark for federated settings,
S. Caldas, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar · 2018
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Decoupled classifiers for group-fair and efficient machine learning
C. Dwork, N. Immorlica, A. T. Kalai, and M. Leiserson · 2018
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Fairness without demographics in repeated loss minimization
T. Hashimoto, M. Srivastava, H. Namkoong, and P. Liang · 2018
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The cost of fairness in binary classification
A. K. Menon and R. C. Williamson · 2018
Addressing algorithmic disparity and performance inconsistency in federated learning
S. Cui, W. Pan, J. Liang, C. Zhang, and F. Wang · 2021
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Minimax group fairness: Algorithms and experiments
E. Diana, W. Gill, M. Kearns, K. Kenthapadi, and A. Roth · 2021
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Retiring adult: New datasets for fair machine learning
F. Ding, M. Hardt, J. Miller, and L. Schmidt · 2021
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Models of fairness in federated learning
K. Donahue and J. Kleinberg · 2021
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Fairness-aware agnostic federated learning
W. Du, D. Xu, X. Wu, and H. Tong · 2021
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Ditto: Fair and robust federated learning through personalization
T. Li, S. Hu, A. Beirami, and V. Smith · 2021
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Fair regression: Quantitative definitions and reduction-based algorithms
A. Agarwal, M. Dudík, and Z. S. Wu · 2019
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Fairness and machine learning. fairmlbook. org
S. Barocas, M. Hardt, and A. Narayanan · 2019
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Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al · 2019
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Agnostic federated learning
M. Mohri, G. Sivek, and A. T. Suresh · 2019
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Federated learning: Challenges, methods, and future directions
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith · 2020
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Fedfair: Training fair models in cross-silo federated learning
L. Chu, L. Wang, Y. Dong, J. Pei, Z. Zhou, and Y. Zhang · 2021
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Enforcing fairness in private federated learning via the modified method of differential multipliers
B. Rodríguez-Gálvez, F. Granqvist, R. van Dalen, and M. Seigel · 2021
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Federated learning of electronic health records to improve mortality prediction in hospitalized patients with covid-19: Machine learning approach
A. Vaid, S. K. Jaladanki, J. Xu, S. Teng, A. Kumar, S. Lee, S. Somani, I. Paranjpe, J. K. De Freitas, T. Wanyan, et al · 2021
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Gifair-fl: An approach for group and individual fairness in federated learning
X. Yue, M. Nouiehed, and R. A. Kontar · 2021
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Improving fairness via federated learning
Y. Zeng, H. Chen, and K. Lee · 2021
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Unified group fairness on federated learning
F. Zhang, K. Kuang, Y. Liu, C. Wu, F. Wu, J. Lu, Y. Shao, and J. Xiao · 2021
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Minimax demographic group fairness in federated learning
A. Papadaki, N. Martinez, M. Bertran, G. Sapiro, and M. Rodrigues · 2022
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