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Recently, lots of algorithms have been proposed for learning a fair classifier from decentralized data.
Fair end-to-end window-based congestion control
J. Mo and J. Walrand · 2000
Earlier work this paper cites.
Hierarchically fair federated learning
J. Zhang, C. Li, A. Robles-Kelly, and M. Kankanhalli · 2004
Earlier work this paper cites.
Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
Earlier work this paper cites.
Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
Earlier work this paper cites.
A data-driven approach to predict the success of bank telemarketing
S. Moro, P. Cortez, and P. Rita · 2014
Earlier work this paper cites.
On the (im)possibility of fairness, 2016
S. A. Friedler, C. Scheidegger, and S. Venkatasubramanian · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
Earlier work this paper cites.
UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
Earlier work this paper cites.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, A. Roth, and Z. S. Wu · 2018
Cited alongside, same era.
The cost of fairness in binary classification
A. K. Menon and R. C. Williamson · 2018
Cited alongside, same era.
N. Guha, A. Talwalkar, and V. Smith · 2019
Cited alongside, same era.
Agnostic federated learning
M. Mohri, G. Sivek, and A. T. Suresh · 2019
Cited alongside, same era.
Unlocking fairness: a trade-off revisited
M. Wick, s. panda, and J.-B. Tristan · 2019
Cited alongside, same era.
Inherent tradeoffs in learning fair representations
H. Zhao and G. Gordon · 2019
Cited alongside, same era.
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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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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Fairness-aware agnostic federated learning
W. Du, D. Xu, X. Wu, and H. Tong · 2021
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Fairfed: Enabling group fairness in federated learning
Y. H. Ezzeldin, S. Yan, C. He, E. Ferrara, and S. Avestimehr · 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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Compas recidivism risk score data and analysis
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Identifying and correcting label bias in machine learning
H. Jiang and O. Nachum · 2020
Cited alongside, same era.
Towards fair and privacy-preserving federated deep models
L. Lyu, J. Yu, K. Nandakumar, Y. Li, X. Ma, J. Jin, H. Yu, and K. Ng · 2020
Cited alongside, same era.
FR-train: A mutual information-based approach to fair and robust training
Y. Roh, K. Lee, S. Whang, and C. Suh · 2020
Cited alongside, same era.
Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models
D. Y. Zhang, Z. Kou, and D. Wang · 2020
Cited alongside, same era.
Fair resource allocation in federated learning
T. Li, M. Sanjabi, A. Beirami, and V. Smith
Cited in the paper.
On the convergence of fedavg on non-iid data
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang
Cited in the paper.
ProPublica · 2021
Closest in time.
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
Closest in time.
Fairbatch: Batch selection for model fairness
Y. Roh, K. Lee, S. E. Whang, and C. Suh · 2021
Closest in time.
Gifair-fl: An approach for group and individual fairness in federated learning
X. Yue, M. Nouiehed, and R. A. Kontar · 2021
Closest in time.