mlsanityData to 7 Oct 2026

Fairness-Driven Private Collaborative Machine Learning

arXiv 2109.14376 ↗First posted 29 Sep 2021cs.LG cs.CR cs.CYcited by 4

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Abstract (arXiv)

The performance of machine learning algorithms can be considerably improved when trained over larger datasets. In many domains, such as medicine and finance, larger datasets can be obtained if several parties, each having access to limited amounts of data, collaborate and share their data. However, such data sharing introduces significant privacy challenges. While multiple recent studies have investigated methods for private collaborative machine learning, the fairness of such collaborative algorithms was overlooked. In this work we suggest a feasible privacy-preserving pre-process mechanism for enhancing fairness of collaborative machine learning algorithms. Our experimentation with the proposed method shows that it is able to enhance fairness considerably with only a minor compromise in accuracy.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 108 references that are in the map. First posted on arXiv 29 Sep 2021.

Builds on

23 of 108 references.

References · 23

  1. 2016Deep Learning with Differential Privacycited by 2,682
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2018A Reductions Approach to Fair Classificationcited by 585
  5. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  6. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  7. 2019Agnostic Federated Learningcited by 451
  8. 2017Algorithmic decision making and the cost of fairnesscited by 402
  9. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  10. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  11. 2015The Variational Fair Autoencodercited by 223
  12. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
11 more
  1. 2020Algorithmic Fairnesscited by 143
  2. 2020Fairness-aware Agnostic Federated Learningcited by 87
  3. 2018Differentially Private Fair Learningcited by 76
  4. 2016Satisfying Real-world Goals with Dataset Constraintscited by 68
  5. 2020Fair Learning with Private Demographic Datacited by 46
  6. 2018Blind Justice: Fairness with Encrypted Sensitive Attributescited by 41
  7. 2021Training Fair Models in Federated Learning without Data Privacy Infringementcited by 26
  8. 2017Penalizing Unfairness in Binary Classificationcited by 25
  9. 2019A Distributed Fair Machine Learning Framework with Private Demographic Data Protectioncited by 8
  10. 2021Achieving Model Fairness in Vertical Federated Learningcited by 8
  11. 2013On the Communication Complexity of Secure Computationcited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
  2. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  3. 2023Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equitycited by 9
  4. 2024A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applicationscited by 0