mlsanityData to 14 Oct 2026

Fairness-aware Federated Minimax Optimization with Convergence Guarantee

arXiv 2307.04417 ↗First posted 10 Jul 2023cs.LG cs.CYcited by 2

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Abstract (arXiv)

Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness issues, where models are biased towards sensitive factors such as race or gender. To tackle this issue, this paper proposes a novel algorithm, fair federated averaging with augmented Lagrangian method (FFALM), designed explicitly to address group fairness issues in FL. Specifically, we impose a fairness constraint on the training objective and solve the minimax reformulation of the constrained optimization problem. Then, we derive the theoretical upper bound for the convergence rate of FFALM. The effectiveness of FFALM in improving fairness is shown empirically on CelebA and UTKFace datasets in the presence of severe statistical heterogeneity.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 10 Jul 2023.

Builds on

20 of 34 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  6. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  7. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  8. 2016Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Conditioncited by 539
  9. 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 429
  10. 2020Fairness in Machine Learning: A Surveycited by 370
  11. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 369
  12. 2021A Field Guide to Federated Optimizationcited by 315
8 more
  1. 2019On Gradient Descent Ascent for Nonconvex-Concave Minimax Problemscited by 270
  2. 2020Fair Attribute Classification through Latent Space De-biasingcited by 125
  3. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
  4. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  5. 2021Improving Fairness via Federated Learningcited by 39
  6. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  7. 2022Differentially Private SGDA for Minimax Problemscited by 23
  8. 2020FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regretcited by 22

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025LoGoFair: Post-Processing for Local and Global Fairness in Federated Learningcited by 1
  2. 2025FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learningcited by 0