mlsanityData to 7 Oct 2026

Minimax Demographic Group Fairness in Federated Learning

arXiv 2201.08304 ↗First posted 20 Jan 2022cs.LG cs.DCcited by 24

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Abstract (arXiv)

Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness in federated learning scenarios where different participating entities may only have access to a subset of the population groups during the training phase. We formally analyze how our proposed group fairness objective differs from existing federated learning fairness criteria that impose similar performance across participants instead of demographic groups. We provide an optimization algorithm -- FedMinMax -- for solving the proposed problem that provably enjoys the performance guarantees of centralized learning algorithms. We experimentally compare the proposed approach against other state-of-the-art methods in terms of group fairness in various federated learning setups, showing that our approach exhibits competitive or superior performance.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 32 of its 48 references that are in the map. First posted on arXiv 20 Jan 2022.

Builds on

32 of 48 references.

References · 32

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  7. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  8. 2019Agnostic Federated Learningcited by 451
  9. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  10. 2019Fair Resource Allocation in Federated Learningcited by 389
  11. 2015The Variational Fair Autoencodercited by 223
  12. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
20 more
  1. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  2. 2020Minimax Pareto Fairness: A Multi Objective Perspectivecited by 152
  3. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
  4. 2021Distributionally Robust Federated Averagingcited by 104
  5. 2020Tilted Empirical Risk Minimizationcited by 103
  6. 2020Robust Federated Learning: The Case of Affine Distribution Shiftscited by 102
  7. 2020Collaborative Fairness in Federated Learningcited by 95
  8. 2020Fairness-aware Agnostic Federated Learningcited by 87
  9. 2021Federated Learning with Fair Averagingcited by 59
  10. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  11. 2021Improving Fairness via Federated Learningcited by 39
  12. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  13. 2021Game of Gradients: Mitigating Irrelevant Clients in Federated Learningcited by 35
  14. 2017Robust Optimization for Non-Convex Objectivescited by 34
  15. 2021Training Fair Models in Federated Learning without Data Privacy Infringementcited by 26
  16. 2020Minimax Group Fairness: Algorithms and Experimentscited by 17
  17. 2021Unified Group Fairness on Federated Learningcited by 15
  18. 2021FedPrune: Towards Inclusive Federated Learningcited by 12
  19. 2021Communication-Efficient Agnostic Federated Averagingcited by 10
  20. 2021Robust Fairness-aware Learning Under Sample Selection Biascited by 9

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Citing papers per year: 2022: 1; 2023: 9; 2024: 10; 2025: 4.

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  1. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  2. 2023Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methodscited by 17
  3. 2022Fair Federated Learning via Bounded Group Losscited by 12
  4. 2023Bias Propagation in Federated Learningcited by 12
  5. 2023Mitigating Group Bias in Federated Learning: Beyond Local Fairnesscited by 10
  6. 2023Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decompositioncited by 10
  7. 2024Differentially Private Worst-group Risk Minimizationcited by 9
  8. 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
  9. 2024PraFFL: A Preference-Aware Scheme in Fair Federated Learningcited by 5
  10. 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
  11. 2023Entropy-driven Fair and Effective Federated Learningcited by 4
  12. 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
12 more
  1. 2024Federated Fairness without Access to Sensitive Groupscited by 2
  2. 2024CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographicscited by 1
  3. 2024PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learningcited by 1
  4. 2025Fairness in Federated Learning: Fairness for Whom?cited by 1
  5. 2023From Utilitarian to Rawlsian Designs for Algorithmic Fairnesscited by 0
  6. 2023Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effectcited by 0
  7. 2024Distribution-Free Fair Federated Learning with Small Samplescited by 0
  8. 2024History-Aware and Dynamic Client Contribution in Federated Learningcited by 0
  9. 2024WassFFed: Wasserstein Fair Federated Learningcited by 0
  10. 2025Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matchingcited by 0
  11. 2025Mitigating Group-Level Fairness Disparities in Federated Visual Language Modelscited by 0
  12. 2025FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluationcited by 0