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Cited by 53Privacy-Preserving Technologies in DataEthics and Social Impacts of AIPrivacy, Security, and Data Protection

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Privacy, accuracy, and model fairness trade-offs in federated learning

Gu, Xiuting, Tianqing, Zhu, Li, Jie, Zhang, Tao, Ren, Wei, Choo, Kim-Kwang Raymond·2022·Computers & Security
DOI 10.1016/j.cose.2022.102907
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Crossref · 10.1016/j.cose.2022.102907

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Citations

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Research works printed but not linked: 23

  1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L., 2016. Deep learning with differential privacy, in: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp. 308–318. · DOI · Paper page
  2. Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., Wallach, H., 2018. A reductions approach to fair classification. arXiv preprint arXiv:1803.02453. · arXiv
  3. Bagdasaryan, E., Poursaeed, O., Shmatikov, V., 2019. Differential privacy has disparate impact on model accuracy. Advances in neural information process-ing systems 32.
  4. Bilal Zafar, M., Valera, I., Gomez Rodriguez, M., Gummadi, K. P., 2015. Fairness constraints: mechanisms for fair classification. arXiv.
  5. Chawla, 2002, Smote: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16, 321, 10.1613/jair.953 · DOI · Paper page
  6. Dwork, 2011, A firm foundation for private data analysis, Commun. ACM, 54, 86, 10.1145/1866739.1866758 · DOI
  7. Caldas, S., Duddu, S.M.K., Wu, P., Li, T., Koneˇcn`y, J., McMahan, H.B., Smith, V., Talwalkar, A., 2018. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097. · arXiv
  8. Eckhouse, 2019, Layers of bias: a unified approach for understanding problems with risk assessment, Crim. Justice Behav., 46, 185, 10.1177/0093854818811379 · DOI · Paper page
  9. Gupta, 2018, Distributed learning of deep neural network over multiple agents, J. Netw. Comput. Appl., 116, 1, 10.1016/j.jnca.2018.05.003 · DOI · Paper page
  10. Geyer, R. C., Klein, T., Nabi, M., 2017a. Differentially private federated learning: a client level perspective. arXiv preprint arXiv:1712.07557 . · arXiv
  11. Jagielski, M., Kearns, M., Mao, J., Oprea, A., Roth, A., Sharifimalvajerdi, S., Ullman, J., 2018. Differentially private fair learning. arXiv: Learning.
  12. 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. Advances and open problems in federated learning. arXiv: Learning.
  13. Kearns, M., Neel, S., Roth, A., Wu, Z. S., 2017. Preventing fairness gerrymandering: auditing and learning for subgroup fairness. arXiv preprint arXiv:1711.05144. · arXiv
  14. Komiyama, J., Shimao, H., 2017. Two-stage algorithm for fairness-aware machine learning. arXiv preprint arXiv:1710.04924. · arXiv
  15. Li, T., Sanjabi, M., Smith, V., 2019. Fair resource allocation in federated learning. arXiv: Learning.
  16. Madras, D., Creager, E., Pitassi, T., Zemel, R., 2018. Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309. · arXiv
  17. Hardt, 2016, Equality of opportunity in supervised learning, Advances in neural information processing systems, 29
  18. McMahan, H.B., Ramage, D., Talwar, K., Zhang, L., 2017b. Learning differentially private recurrent language models. arXiv preprint arXiv:1710.0696.
  19. Brendan McMahan, H., Moore, E., Ramage, D., Hampson, S., Ag¨uera y Arcas, B., 2016. Communication-efficient learning of deep networks from decentralized data. arXiv e-prints , arXiv–1602.
  20. Williamson, 2019, Fairness risk measures, in: International Conference on Machine Learning, PMLR, 6786
  21. Zhang, 2020, Fairness in semi-supervised learning: unlabeled data help to reduce discrimination, IEEE Trans. Knowl. Data Eng., 34, 1763, 10.1109/TKDE.2020.3002567 · DOI
  22. Zhang, 2021, Balancing learning model privacy, fairness, and accuracy with early stopping criteria, IEEE Trans. Neural Netw. Learn. Syst., 1
  23. Zhang, 2020, Fairness in semi-supervised learning: unlabeled data help to reduce discrimination, IEEE Trans. Knowl. Data Eng., 34, 1763, 10.1109/TKDE.2020.3002567 · DOI

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Metadata

Authors
Gu, X., Tianqing, Z., Li, J., Zhang, T., Ren, W., & Choo, K.-K. R.
Affiliations
  • China University of Geosciences (CN)
  • University of Technology Sydney (AU)
  • The University of Texas at San Antonio (US)
ORCID
Venue
Computers & Security
Publisher
Elsevier BV
Published
2022-11
Volume
122
Pages
102907
ISSN
0167-4048 (ISSN-L)
DOI
10.1016/j.cose.2022.102907
OpenAlex
W4294653533
Cited by
53
References
1
Citations by year
2026: 16 · 2025: 17 · 2024: 12 · 2023: 8
FWCI
3.04
Type
journal article
Language
en
Open access
closed
Licence
Locations
Computers & Security (publishedVersion)
Funders
National Natural Science Foundation of China (61972366)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
Keywords
federated learning, differential privacy, model fairness, privacy-utility trade-off, local differential privacy, demographic parity, equal opportunities, class imbalance, privacy-accuracy trade-off

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