mlsanityData to 7 Oct 2026

Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning

arXiv 2108.08435 ↗First posted 19 Aug 2021cs.LGcited by 50

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Abstract (arXiv)

Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples across different sources. So far FL research has mostly focused on improving the performance, how the algorithmic disparity will be impacted for the model learned from FL and the impact of algorithmic disparity on the utility inconsistency are largely unexplored. In this paper, we propose an FL framework to jointly consider performance consistency and algorithmic fairness across different local clients (data sources). We derive our framework from a constrained multi-objective optimization perspective, in which we learn a model satisfying fairness constraints on all clients with consistent performance. Specifically, we treat the algorithm prediction loss at each local client as an objective and maximize the worst-performing client with fairness constraints through optimizing a surrogate maximum function with all objectives involved. A gradient-based procedure is employed to achieve the Pareto optimality of this optimization problem. Theoretical analysis is provided to prove that our method can converge to a Pareto solution that achieves the min-max performance with fairness constraints on all clients. Comprehensive experiments on synthetic and real-world datasets demonstrate the superiority that our approach over baselines and its effectiveness in achieving both fairness and consistency across all local clients.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Builds on 17 of its 40 references that are in the map. First posted on arXiv 19 Aug 2021.

Builds on

17 of 40 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 816
  4. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  5. 2019Agnostic Federated Learningcited by 451
  6. 2019Fair Resource Allocation in Federated Learningcited by 389
  7. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  8. 2019Pareto Multi-Task Learningcited by 224
  9. 2015The Variational Fair Autoencodercited by 223
  10. 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representationscited by 165
  11. 2020Minimax Pareto Fairness: A Multi Objective Perspectivecited by 152
  12. 2020Fairness-aware Agnostic Federated Learningcited by 87
5 more
  1. 2019Putting Fairness Principles into Practice: Challenges, Metrics, and Improvementscited by 70
  2. 2019The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metriccited by 41
  3. 2021Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decompositioncited by 17
  4. 2018Generalizability of predictive models for intensive care unit patientscited by 9
  5. 2020Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utilitycited by 9

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Cited by year

Citing papers per year: 2021: 3; 2022: 8; 2023: 11; 2024: 16; 2025: 12.

50 papers.

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  1. 2021Towards Fairness-Aware Federated Learningcited by 57
  2. 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
  3. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  4. 2021Improving Fairness via Federated Learningcited by 39
  5. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  6. 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 28
  7. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  8. 2022Minimax Demographic Group Fairness in Federated Learningcited by 24
  9. 2022Local Stochastic Bilevel Optimization with Momentum-Based Variance Reductioncited by 20
  10. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  11. 2022PrivFairFL: Privacy-Preserving Group Fairness in Federated Learningcited by 13
  12. 2025Do We Really Need to Design New Byzantine-robust Aggregation Rules?cited by 13
38 more
  1. 2022Fair Federated Learning via Bounded Group Losscited by 12
  2. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
  3. 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 12
  4. 2023Federated Multi-Objective Learningcited by 11
  5. 2023Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decompositioncited by 10
  6. 2022Towards Multi-Objective Statistically Fair Federated Learningcited by 8
  7. 2022FedDAR: Federated Domain-Aware Representation Learningcited by 8
  8. 2022A Fair Federated Learning Framework With Reinforcement Learningcited by 6
  9. 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
  10. 2024GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learningcited by 5
  11. 2024PraFFL: A Preference-Aware Scheme in Fair Federated Learningcited by 5
  12. 2024PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learningcited by 4
  13. 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 4
  14. 2023Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Datacited by 3
  15. 2024Federated Communication-Efficient Multi-Objective Optimizationcited by 3
  16. 2024FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Predictioncited by 3
  17. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  18. 2025Multi-objective methods in Federated Learning: A survey and taxonomycited by 3
  19. 2023Fairness-aware Federated Minimax Optimization with Convergence Guaranteecited by 2
  20. 2024Federated Fairness without Access to Sensitive Groupscited by 2
  21. 2025AFed: Algorithmic Fair Federated Learningcited by 2
  22. 2025The Cost of Local and Global Fairness in Federated Learningcited by 2
  23. 2024Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learningcited by 1
  24. 2024CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographicscited by 1
  25. 2024Semi-Variance Reduction for Fair Federated Learningcited by 1
  26. 2024Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clusteringcited by 1
  27. 2024PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learningcited by 1
  28. 2025pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learningcited by 1
  29. 2025LoGoFair: Post-Processing for Local and Global Fairness in Federated Learningcited by 1
  30. 2025Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneitycited by 1
  31. 2023SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learningcited by 0
  32. 2023Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effectcited by 0
  33. 2024Distribution-Free Fair Federated Learning with Small Samplescited by 0
  34. 2024WassFFed: Wasserstein Fair Federated Learningcited by 0
  35. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  36. 2025FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learningcited by 0
  37. 2025FAIRVAR: Fair Federated Learning via Variance Regularizationcited by 0
  38. 2025FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphscited by 0