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Federated learning with differential privacy, or private federated learning, provides a strategy to train machine learning models while respecting users' privacy.
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Calibrating noise to sensitivity in private data analysis
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A variational approach to privacy and fairness
B. Rodríguez-Gálvez, R. Thobaben, and M. Skoglund · 2006
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The algorithmic foundations of differential privacy
C. Dwork, A. Roth, et al · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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A comprehensive comparison of multiparty secure additions with differential privacy
S. Goryczka and L. Xiong · 2015
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017
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Semantics derived automatically from language corpora contain human-like biases
A. Caliskan, J. J. Bryson, and A. Narayanan · 2017
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Emnist: Extending mnist to handwritten letters
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
M. B. Zafar, I. Valera, M. Gomez Rodriguez, and K. P. Gummadi · 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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Protection against reconstruction and its applications in private federated learning
A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, and R. Rogers · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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Subsampled Rényi differential privacy and analytical moments accountant
Y.-X. Wang, B. Balle, and S. P. Kasiviswanathan · 2019
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Mitigating bias in federated learning
A. Abay, Y. Zhou, N. Baracaldo, S. Rajamoni, E. Chuba, and H. Ludwig · 2020
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Differentially private and fair classification via calibrated functional mechanism
J. Ding, X. Zhang, X. Li, J. Wang, R. Yu, and M. Pan · 2020
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Lagrangian duality for constrained deep learning
F. Fioretto, P. Van Hentenryck, T. W. Mak, C. Tran, F. Baldo, and M. Lombardi · 2020
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Improving on-device speaker verification using federated learning with privacy
F. Granqvist, M. Seigel, R. van Dalen, Á. Cahill, S. Shum, and M. Paulik · 2020
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S. Caldas, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar · 2018
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Fairness definitions explained
S. Verma and J. Rubin · 2018
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Differential privacy has disparate impact on model accuracy
E. Bagdasaryan, O. Poursaeed, and V. Shmatikov · 2019
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On the compatibility of privacy and fairness
R. Cummings, V. Gupta, D. Kimpara, and J. Morgenstern · 2019
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Differentially private fair learning
M. Jagielski, M. Kearns, J. Mao, A. Oprea, A. Roth, S. Sharifi-Malvajerdi, and J. Ullman · 2019
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Fair resource allocation in federated learning
T. Li, M. Sanjabi, A. Beirami, and V. Smith · 2019
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Private selection from private candidates
J. Liu and K. Talwar · 2019
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FedMGDA+: Federated learning meets multi-objective optimization
Z. Hu, K. Shaloudegi, G. Zhang, and Y. Yu · 2020
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Fairness and accuracy in federated learning
W. Huang, T. Li, D. Wang, S. Du, and J. Zhang · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, İ. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors · 2020
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The zoo of fairness metrics in machine learning
A. Castelnovo, R. Crupi, G. Greco, and D. Regoli · 2021
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Addressing algorithmic disparity and performance inconsistency in federated learning, 2021
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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Ditto: Fair and robust federated learning through personalization
T. Li, S. Hu, A. Beirami, and V. Smith · 2021
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Differentially private and fair deep learning: A Lagrangian dual approach
C. Tran, F. Fioretto, and P. Van Hentenryck · 2021
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