mlsanityData to 14 Oct 2026

Federated Learning Meets Fairness and Differential Privacy

arXiv 2108.09932 ↗First posted 23 Aug 2021cs.LG cs.AI cs.CRcited by 9

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Abstract (arXiv)

Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy. Researchers tackle these issues by introducing fairness metrics, or federated learning, or differential privacy. A first, this work presents an ethical federated learning model, incorporating all three measures simultaneously. Experiments on the Adult, Bank and Dutch datasets highlight the resulting ``empirical interplay" between accuracy, fairness, and privacy.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 23 Aug 2021.

Builds on

16 of 34 references.

References · 16

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  5. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  6. 2018A Reductions Approach to Fair Classificationcited by 587
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  8. 2019Fair Resource Allocation in Federated Learningcited by 394
  9. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  10. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  11. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  12. 2020Fairness-aware Agnostic Federated Learningcited by 87
4 more
  1. 2019Federated Learning with Bayesian Differential Privacycited by 60
  2. 2020Differentially Private and Fair Deep Learning: A Lagrangian Dual Approachcited by 53
  3. 2020Fair Learning with Private Demographic Datacited by 46
  4. 2018FNNC: Achieving Fairness through Neural Networkscited by 43

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 1; 2024: 2; 2025: 1.

9 papers.

  1. 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Surveycited by 47
  2. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  3. 2022PrivFairFL: Privacy-Preserving Group Fairness in Federated Learningcited by 13
  4. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  5. 2024PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learningcited by 4
  6. 2022"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learningcited by 3
  7. 2022Differentially Private Federated Combinatorial Bandits with Constraintscited by 3
  8. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  9. 2025Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AIcited by 0