mlsanityData to 14 Oct 2026

Privacy and Fairness in Federated Learning: on the Perspective of Trade-off

arXiv 2306.14123 ↗First posted 25 Jun 2023cs.LG cs.AI cs.CRcited by 26

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Abstract (arXiv)

Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair results, with most research focusing on one or the other. As two crucial ethical notions, the interactions between privacy and fairness are comparatively less studied. However, since privacy and fairness compete, considering each in isolation will inevitably come at the cost of the other. To provide a broad view of these two critical topics, we presented a detailed literature review of privacy and fairness issues, highlighting unique challenges posed by FL and solutions in federated settings. We further systematically surveyed different interactions between privacy and fairness, trying to reveal how privacy and fairness could affect each other and point out new research directions in fair and private FL.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 153 of its 237 references that are in the map. First posted on arXiv 25 Jun 2023.

Builds on

153 of 237 references.

References · 153

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38 more
  1. 2017Counterfactual Fairnesscited by 804
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  3. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
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50 most cited of 153 All 153 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 9; 2026: 5.

26 papers.

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  1. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 103
  2. 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 30
  3. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 15
  4. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  5. 2023SoK: Unintended Interactions among Machine Learning Defenses and Riskscited by 6
  6. 2023Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learningcited by 4
  7. 2024Pursuing Overall Welfare in Federated Learning through Sequential Decision Makingcited by 2
  8. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  9. 2024Combining Machine Learning Defenses without Conflictscited by 2
  10. 2025FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Riskcited by 2
  11. 2025Fairness in Augmented Graph Learning: A Surveycited by 2
  12. 2024CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographicscited by 1
14 more
  1. 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learningcited by 1
  2. 2025RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utilitycited by 1
  3. 2025Fairness in Federated Learning: Fairness for Whom?cited by 1
  4. 2025Amulet: a Python Library for Assessing Interactions Among ML Defenses and Riskscited by 1
  5. 2024GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learningcited by 0
  6. 2025Testing for Causal Fairnesscited by 0
  7. 2025Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AIcited by 0
  8. 2025The More is not the Merrier: Investigating the Effect of Client Size on Federated Learningcited by 0
  9. 2025Private Federated Multiclass Post-hoc Calibrationcited by 0
  10. 2026When the Server Steps In: Calibrated Updates for Fair Federated Learningcited by 0
  11. 2026Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learningcited by 0
  12. 2026SoK: Colluding Adversaries in Machine Learning Pipelinescited by 0
  13. 2026Quantization in Federated Learning: Methods, Challenges and Future Directionscited by 0
  14. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0