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Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment.
Fair resource allocation in federated learning
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Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
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Adaptive federated optimization
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Fedml: A research library and benchmark for federated machine learning
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Mitigating bias in federated learning
Abay, A.; Zhou, Y.; Baracaldo, N.; Rajamoni, S.; Chuba, E.; and Ludwig, H. 2020 · 2012
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Fairness through awareness
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Data preprocessing techniques for classification without discrimination
Kamiran, F.; and Calders, T. 2012 · 2012
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Fairness-aware classifier with prejudice remover regularizer
Kamishima, T.; Akaho, S.; Asoh, H.; and Sakuma, J. 2012 · 2012
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Certifying and removing disparate impact
Feldman, M.; Friedler, S. A.; Moeller, J.; Scheidegger, C.; and Venkatasubramanian, S. 2015 · 2015
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; Srebro, N.; et al. 2016 · 2016
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How we analyzed the COMPAS recidivism algorithm
Larson, J.; Mattu, S.; Kirchner, L.; and Angwin, J. 2016 · 2016
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Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K.; Ivanov, V.; Kreuter, B.; Marcedone, A.; McMahan, H. B.; Patel, S.; Ramage, D.; Segal, A.; and Seth, K. 2017 · 2017
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UCI Machine Learning Repository
Dua, D.; and Graff, C. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning
Grgić-Hlača, N.; Zafar, M. B.; Gummadi, K. P.; and Weller, A. 2018 · 2018
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TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers
A geometric solution to fair representations
He, Y.; Burghardt, K.; and Lerman, K. 2020 · 2020
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Collaborative fairness in federated learning
Lyu, L.; Xu, X.; Wang, Q.; and Yu, H. 2020 · 2020
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Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models
Zhang, D. Y.; Kou, Z.; and Wang, D. 2020 · 2020
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Retiring Adult: New Datasets for Fair Machine Learning
Ding, F.; Hardt, M.; Miller, J.; and Schmidt, L. 2021 · 2021
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Fairness-aware Agnostic Federated Learning
Du, W.; Xu, D.; Wu, X.; and Tong, H. 2021 · 2021
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Enforcing fairness in private federated learning via the modified method of differential multipliers
Gálvez, B. R.; Granqvist, F.; van Dalen, R.; and Seigel, M. 2021 · 2021
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Mundnich, K.; Booth, B. M.; l’Hommedieu, M.; Feng, T.; Girault, B.; L’hommedieu, J.; Wildman, M.; Skaaden, S.; Nadarajan, A.; Villatte, J. L.; et al. 2020 · 2018
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Mitigating unwanted biases with adversarial learning
Zhang, B. H.; Lemoine, B.; and Mitchell, M. 2018 · 2018
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Multiaccuracy: Black-box post-processing for fairness in classification
Kim, M. P.; Ghorbani, A.; and Zou, J. 2019 · 2019
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Bias mitigation post-processing for individual and group fairness
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Agnostic federated learning
Mohri, M.; Sivek, G.; and Suresh, A. T. 2019 · 2019
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Federated Learning with Fair Averaging
Wang, Z.; Fan, X.; Qi, J.; Wen, C.; Wang, C.; and Yu, R. 2021b
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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. B.; Cormode, G.; Cummings, R.; D’Oliveira, R. G. L.; Rouayheb, S. Y. E.; Evans, D.; Gardner, J.; Garrett, Z.; Gascón, A.; Ghazi, B.; Gibbons, P. B.; Gruteser, M.; Harchaoui, Z.; He, C.; He, L.; Huo, Z.; Hutchinson, B.; Hsu, J.; Jaggi, M.; Javidi, T.; Joshi, G.; Khodak, M.; Konecný, J.; Korolova, A.; Koushanfar, F.; Koyejo, O.; Lepoint, T.; Liu, Y.; Mittal, P.; Mohri, M.; Nock, R.; Özgür, A.; Pagh, R.; Raykova, M.; Qi, H.; Ramage, D.; Raskar, R.; Song, D. X.; Song, W.; Stich, S. U.; Sun, Z.; Suresh, A. T.; Tramèr, F.; Vepakomma, P.; Wang, J.; Xiong, L.; Xu, Z.; Yang, Q.; Yu, F. X.; Yu, H.; and Zhao, S. 2021 · 2021
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Federating for Learning Group Fair Models
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FairBatch: Batch Selection for Model Fairness
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Improving fairness via federated learning
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