mlsanityData to 14 Oct 2026

Mitigating Bias in Federated Learning

arXiv 2012.02447 ↗First posted 4 Dec 2020cs.LG stat.MLcited by 56

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Abstract (arXiv)

As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored. FL is a rising approach for collaborative ML, in which an aggregator orchestrates multiple parties to train a global model without sharing their training data. In this paper, we discuss causes of bias in FL and propose three pre-processing and in-processing methods to mitigate bias, without compromising data privacy, a key FL requirement. As data heterogeneity among parties is one of the challenging characteristics of FL, we conduct experiments over several data distributions to analyze their effects on model performance, fairness metrics, and bias learning patterns. We conduct a comprehensive analysis of our proposed techniques, the results demonstrating that these methods are effective even when parties have skewed data distributions or as little as 20% of parties employ the methods.

Key facts. Cited by 56 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Dec 2020.

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Citing papers per year: 2021: 9; 2022: 9; 2023: 12; 2024: 16; 2025: 9; 2026: 1.

56 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
  3. 2021Federated Learning with Fair Averagingcited by 61
  4. 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Surveycited by 47
  5. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  6. 2021FedJAX: Federated learning simulation with JAXcited by 32
  7. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 31
  8. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  9. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
  10. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  11. 2022Local Stochastic Bilevel Optimization with Momentum-Based Variance Reductioncited by 20
  12. 2023Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methodscited by 17
38 more
  1. 2025Do We Really Need to Design New Byzantine-robust Aggregation Rules?cited by 15
  2. 2021FLoRA: Single-shot Hyper-parameter Optimization for Federated Learningcited by 14
  3. 2022PrivFairFL: Privacy-Preserving Group Fairness in Federated Learningcited by 13
  4. 2023Bias Propagation in Federated Learningcited by 12
  5. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  6. 2021Communication-Efficient Agnostic Federated Averagingcited by 10
  7. 2022FAIR-FATE: Fair Federated Learning with Momentumcited by 10
  8. 2023Mitigating Group Bias in Federated Learning: Beyond Local Fairnesscited by 10
  9. 2023Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decompositioncited by 10
  10. 2024Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Studycited by 10
  11. 2021Models of fairness in federated learningcited by 8
  12. 2024Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Modelscited by 7
  13. 2023Incentivizing Honesty among Competitors in Collaborative Learning and Optimizationcited by 6
  14. 2023Scaff-PD: Communication Efficient Fair and Robust Federated Learningcited by 6
  15. 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
  16. 2024GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learningcited by 5
  17. 2024PraFFL: A Preference-Aware Scheme in Fair Federated Learningcited by 5
  18. 2024Achieving Fairness Across Local and Global Models in Federated Learningcited by 5
  19. 2022FedSS: Federated Learning with Smart Selection of clientscited by 4
  20. 2023Multi-dimensional Fair Federated Learningcited by 4
  21. 2024Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processingcited by 4
  22. 2024PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learningcited by 4
  23. 2024EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learningcited by 4
  24. 2022Towards an Accountable and Reproducible Federated Learning: A FactSheets Approachcited by 3
  25. 2022E2FL: Equal and Equitable Federated Learningcited by 3
  26. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  27. 2024Dataset Distillation-based Hybrid Federated Learning on Non-IID Datacited by 3
  28. 2024Incentivizing Truthful Collaboration in Heterogeneous Federated Learningcited by 3
  29. 2025Fairness in Federated Learning: Trends, Challenges, and Opportunitiescited by 3
  30. 2024Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learningcited by 2
  31. 2025The Cost of Local and Global Fairness in Federated Learningcited by 2
  32. 2021Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasetscited by 1
  33. 2023Privacy-Preserving Load Forecasting via Personalized Model Obfuscationcited by 1
  34. 2024CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographicscited by 1
  35. 2024Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clusteringcited by 1
  36. 2025pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learningcited by 1
  37. 2025Fairness in Federated Learning: Fairness for Whom?cited by 1
  38. 2023Fair Differentially Private Federated Learning Frameworkcited by 0

50 most cited of 56 All 56 (JSON)