mlsanityData to 14 Oct 2026

PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

arXiv 2205.11584 ↗First posted 23 May 2022cs.LG cs.CRcited by 13

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Abstract (arXiv)

Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity. Achieving group fairness in Federated Learning (FL) is challenging because mitigating bias inherently requires using the sensitive attribute values of all clients, while FL is aimed precisely at protecting privacy by not giving access to the clients' data. As we show in this paper, this conflict between fairness and privacy in FL can be resolved by combining FL with Secure Multiparty Computation (MPC) and Differential Privacy (DP). In doing so, we propose a method for training group-fair ML models in cross-device FL under complete and formal privacy guarantees, without requiring the clients to disclose their sensitive attribute values.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 27 of its 71 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

27 of 71 references.

References · 27

  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. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  7. 2018A Reductions Approach to Fair Classificationcited by 587
  8. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  9. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  10. 2019Agnostic Federated Learningcited by 456
  11. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  12. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 384
15 more
  1. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  2. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  3. 2020Algorithmic Fairnesscited by 143
  4. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 127
  5. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
  6. 2020Fairness-aware Agnostic Federated Learningcited by 87
  7. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 85
  8. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  9. 2020Mitigating Bias in Federated Learningcited by 56
  10. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  11. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  12. 2019Secure Evaluation of Quantized Neural Networkscited by 22
  13. 2021Unified Group Fairness on Federated Learningcited by 15
  14. 2021Federated Learning Meets Fairness and Differential Privacycited by 9
  15. 2021Federating for Learning Group Fair Modelscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 4; 2025: 6; 2026: 1.

13 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2024Differentially Private Clustered Federated Learningcited by 4
  3. 2024PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learningcited by 4
  4. 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
  5. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  6. 2025Global Group Fairness in Federated Learning via Function Trackingcited by 1
  7. 2025RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utilitycited by 1
  8. 2025Fairness in Federated Learning: Fairness for Whom?cited by 1
  9. 2023Fair Differentially Private Federated Learning Frameworkcited by 0
  10. 2025Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AIcited by 0
  11. 2025FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluationcited by 0
  12. 2025The Fair Game: Auditing & Debiasing AI Algorithms Over Timecited by 0
1 more
  1. 2026Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learningcited by 0