mlsanityData to 7 Oct 2026

Towards Fairness-Aware Federated Learning

arXiv 2111.01872 ↗First posted 2 Nov 2021cs.LG cs.AI cs.DCcited by 57

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Abstract (arXiv)

Recent advances in Federated Learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy guarantees. However, most current works focus on the interest of the central controller in FL,and overlook the interests of the FL clients. This may result in unfair treatment of clients that discourages them from actively participating in the learning process and damages the sustainability of the FL ecosystem. Therefore, the topic of ensuring fairness in FL is attracting a great deal of research interest. In recent years, diverse Fairness-Aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different perspectives. However, there is no comprehensive survey that helps readers gain insight into this interdisciplinary field. This paper aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches, and suggest promising future research directions towards FAFL.

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). Builds on 59 of its 111 references that are in the map. First posted on arXiv 2 Nov 2021.

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59 of 111 references.

References · 59

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  6. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  7. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  8. 2020Gradient Surgery for Multi-Task Learningcited by 1,210
  9. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  10. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  11. 2021Towards Personalized Federated Learningcited by 462
  12. 2019Agnostic Federated Learningcited by 451
38 more
  1. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  2. 2019Fair Resource Allocation in Federated Learningcited by 389
  3. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  4. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  5. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  6. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 279
  7. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  8. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  9. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  10. 2020Threats to Federated Learning: A Surveycited by 171
  11. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 162
  12. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  13. 2019Fairness in Deep Learning: A Computational Perspectivecited by 123
  14. 2019Data Valuation using Reinforcement Learningcited by 115
  15. 2020Collaborative Fairness in Federated Learningcited by 95
  16. 2019Reliable Federated Learning for Mobile Networkscited by 92
  17. 2020Fairness-aware Agnostic Federated Learningcited by 87
  18. 2019Measure Contribution of Participants in Federated Learningcited by 78
  19. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  20. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 73
  21. 2019Active Federated Learningcited by 63
  22. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 61
  23. 2021Federated Learning with Fair Averagingcited by 59
  24. 2020Federated learning with class imbalance reductioncited by 52
  25. 2020Federated Learning Meets Multi-objective Optimizationcited by 50
  26. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  27. 2020FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MECcited by 48
  28. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  29. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  30. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  31. 2020Communication-Efficient Federated Learning via Optimal Client Samplingcited by 39
  32. 2020An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approachcited by 39
  33. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  34. 2020Stochastic Client Selection for Federated Learning with Volatile Clientscited by 38
  35. 2020A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learningcited by 38
  36. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  37. 2020Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learningcited by 37
  38. 2019A Crowdsourcing Framework for On-Device Federated Learningcited by 32

50 most cited of 59 All 59 (JSON)

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Citing papers per year: 2022: 1; 2023: 14; 2024: 19; 2025: 15; 2026: 8.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2023FedGH: Heterogeneous Federated Learning with Generalized Global Headercited by 56
  3. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  4. 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 28
  5. 2023Fairness-Aware Client Selection for Federated Learningcited by 15
  6. 2025FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Modelscited by 11
  7. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  8. 2023pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharingcited by 9
  9. 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approachcited by 8
  10. 2023Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learningcited by 7
  11. 2024Federated Unlearning: a Perspective of Stability and Fairnesscited by 7
  12. 2025Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networkscited by 6
38 more
  1. 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
  2. 2023Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropoutcited by 4
  3. 2023Trustworthy Federated Learning: A Surveycited by 4
  4. 2023A Survey on Image-text Multimodal Modelscited by 4
  5. 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
  6. 2023Fairness and Privacy in Federated Learning and Their Implications in Healthcarecited by 3
  7. 2024FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Predictioncited by 3
  8. 2023A Framework for Incentivized Collaborative Learningcited by 2
  9. 2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
  10. 2024An Auction-based Marketplace for Model Trading in Federated Learningcited by 2
  11. 2024Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learningcited by 2
  12. 2024SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learningcited by 2
  13. 2024Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalizationcited by 2
  14. 2025Fairness in Augmented Graph Learning: A Surveycited by 2
  15. 2023Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learningcited by 1
  16. 2024Fairness-Aware Job Scheduling for Multi-Job Federated Learningcited by 1
  17. 2024HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learningcited by 1
  18. 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learningcited by 1
  19. 2024Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clusteringcited by 1
  20. 2025Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learningcited by 1
  21. 2025pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learningcited by 1
  22. 2025Towards Unbiased Federated Graph Learning: Label and Topology Perspectivescited by 1
  23. 2025PSI-PFL: Population Stability Index for Client Selection in non-IID Personalized Federated Learningcited by 1
  24. 2025On the Fragility of Contribution Score Computation in Federated Learningcited by 1
  25. 2025FeDABoost: Fairness Aware Federated Learning with Adaptive Boostingcited by 1
  26. 2026APPA: Adaptive Preference Pluralistic Alignment for Fair Federated RLHF of LLMscited by 1
  27. 2023IP-FL: Incentivized and Personalized Federated Learningcited by 0
  28. 2024Distributed client selection with multi-objective in federated learning assisted Internet of Vehiclescited by 0
  29. 2024A Bargaining-based Approach for Feature Trading in Vertical Federated Learningcited by 0
  30. 2024Data Quality in Edge Machine Learning: A State-of-the-Art Surveycited by 0
  31. 2024Heterogeneity: An Open Challenge for Federated On-board Machine Learningcited by 0
  32. 2024Alpha and Prejudice: Improving α-sized Worst-case Fairness via Intrinsic Reweightingcited by 0
  33. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  34. 2025Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Modelscited by 0
  35. 2025Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learningcited by 0
  36. 2025FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networkscited by 0
  37. 2025Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?cited by 0
  38. 2025FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphscited by 0

50 most cited of 57 All 57 (JSON)