mlsanityData to 14 Oct 2026

Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning

arXiv 2311.18190 ↗First posted 30 Nov 2023cs.LG cs.AIcited by 4

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Abstract (arXiv)

Federated Learning (FL) is a novel privacy-protection distributed machine learning paradigm that guarantees user privacy and prevents the risk of data leakage due to the advantage of the client's local training. Researchers have struggled to design fair FL systems that ensure fairness of results. However, the interplay between fairness and privacy has been less studied. Increasing the fairness of FL systems can have an impact on user privacy, while an increase in user privacy can affect fairness. In this work, on the client side, we use fairness metrics, such as Demographic Parity (DemP), Equalized Odds (EOs), and Disparate Impact (DI), to construct the local fair model. To protect the privacy of the client model, we propose a privacy-protection fairness FL method. The results show that the accuracy of the fair model with privacy increases because privacy breaks the constraints of the fairness metrics. In our experiments, we conclude the relationship between privacy, fairness and utility, and there is a tradeoff between these.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 39 of its 49 references that are in the map. First posted on arXiv 30 Nov 2023.

Builds on

39 of 49 references.

References · 39

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  4. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  5. 2018A Reductions Approach to Fair Classificationcited by 587
  6. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  7. 2019Fair Resource Allocation in Federated Learningcited by 394
  8. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  9. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  10. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
  11. 2018Amplification by Shuffling: From Local to Central Differential Privacy via Anonymitycited by 164
  12. 2020Minimax Pareto Fairness: A Multi Objective Perspectivecited by 153
27 more
  1. 2018Distributed Differential Privacy via Shufflingcited by 115
  2. 2020Robust Optimization for Fairness with Noisy Protected Groupscited by 78
  3. 2018Differentially Private Fair Learningcited by 76
  4. 2019The Privacy Blanket of the Shuffle Modelcited by 74
  5. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  6. 2020Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacycited by 61
  7. 2019Fair Decision Making using Privacy-Protected Datacited by 58
  8. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  9. 2020Differentially Private and Fair Deep Learning: A Lagrangian Dual Approachcited by 53
  10. 2019Equalized odds postprocessing under imperfect group informationcited by 48
  11. 2021Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Datacited by 48
  12. 2020Fair Learning with Private Demographic Datacited by 46
  13. 2018FNNC: Achieving Fairness through Neural Networkscited by 43
  14. 2018Blind Justice: Fairness with Encrypted Sensitive Attributescited by 41
  15. 2019Noise-tolerant fair classificationcited by 40
  16. 2021FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learningcited by 39
  17. 2022The Fundamental Price of Secure Aggregation in Differentially Private Federated Learningcited by 39
  18. 2020Private Summation in the Multi-Message Shuffle Modelcited by 34
  19. 2022Disparate Impact in Differential Privacy from Gradient Misalignmentcited by 31
  20. 2021PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakagecited by 28
  21. 2022Private Non-Convex Federated Learning Without a Trusted Servercited by 26
  22. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  23. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  24. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  25. 2021Multiaccurate Proxies for Downstream Fairnesscited by 17
  26. 2022Stochastic Differentially Private and Fair Learningcited by 16
  27. 2020Privacy Enhancement via Dummy Points in the Shuffle Modelcited by 2

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2024PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learningcited by 4
  2. 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Modelscited by 3
  3. 2025RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utilitycited by 1
  4. 2025FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utilitycited by 0