Fairness-aware Agnostic Federated Learning
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Abstract (arXiv)
Federated learning is an emerging framework that builds centralized machine learning models with training data distributed across multiple devices. Most of the previous works about federated learning focus on the privacy protection and communication cost reduction. However, how to achieve fairness in federated learning is under-explored and challenging especially when testing data distribution is different from training distribution or even unknown. Introducing simple fairness constraints on the centralized model cannot achieve model fairness on unknown testing data. In this paper, we develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution. We use kernel reweighing functions to assign a reweighing value on each training sample in both loss function and fairness constraint. Therefore, the centralized model built from AgnosticFair can achieve high accuracy and fairness guarantee on unknown testing data. Moreover, the built model can be directly applied to local sites as it guarantees fairness on local data distributions. To our best knowledge, this is the first work to achieve fairness in federated learning. Experimental results on two real datasets demonstrate the effectiveness in terms of both utility and fairness under data shift scenarios.
Key facts. Cited by 87 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2020.
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Citing papers per year: 2021: 17; 2022: 13; 2023: 21; 2024: 17; 2025: 13; 2026: 6.
87 papers.
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- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
- 2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
- 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
- 2021Towards Fairness-Aware Federated Learningcited by 57
- 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
- 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
- 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
- 2021Improving Fairness via Federated Learningcited by 39
- 2022Test-Time Robust Personalization for Federated Learningcited by 39
- 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
- 2021FedJAX: Federated learning simulation with JAXcited by 32
- 2021Training Fair Models in Federated Learning without Data Privacy Infringementcited by 26
38 more
- 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
- 2022Minimax Demographic Group Fairness in Federated Learningcited by 24
- 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
- 2022Fairness in Federated Learning via Core-Stabilitycited by 22
- 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
- 2022Local Stochastic Bilevel Optimization with Momentum-Based Variance Reductioncited by 20
- 2023No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendationcited by 18
- 2021Unified Group Fairness on Federated Learningcited by 15
- 2023Benchmarking Algorithms for Federated Domain Generalizationcited by 14
- 2022PrivFairFL: Privacy-Preserving Group Fairness in Federated Learningcited by 13
- 2023Optimal Multi-Distribution Learningcited by 13
- 2022Fair Federated Learning via Bounded Group Losscited by 12
- 2023Bias Propagation in Federated Learningcited by 12
- 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 12
- 2023Mitigating Group Bias in Federated Learning: Beyond Local Fairnesscited by 10
- 2023Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decompositioncited by 10
- 2021Robust Fairness-aware Learning Under Sample Selection Biascited by 9
- 2021Federated Learning Meets Fairness and Differential Privacycited by 9
- 2022On the Generalization of Wasserstein Robust Federated Learningcited by 9
- 2022Federated Data Analytics: A Study on Linear Modelscited by 9
- 2023Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problemscited by 9
- 2021Achieving Model Fairness in Vertical Federated Learningcited by 8
- 2021Models of fairness in federated learningcited by 8
- 2022FedDAR: Federated Domain-Aware Representation Learningcited by 8
- 2023Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Modelscited by 8
- 2022Reward Systems for Trustworthy Medical Federated Learningcited by 6
- 2021Blockchain-based Trustworthy Federated Learning Architecturecited by 5
- 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
- 2024GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learningcited by 5
- 2024PraFFL: A Preference-Aware Scheme in Fair Federated Learningcited by 5
- 2024Achieving Fairness Across Local and Global Models in Federated Learningcited by 5
- 2021Fairness-Driven Private Collaborative Machine Learningcited by 4
- 2023Entropy-driven Fair and Effective Federated Learningcited by 4
- 2023Federated Learning with Convex Global and Local Constraintscited by 4
- 2023Multi-dimensional Fair Federated Learningcited by 4
- 2024Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processingcited by 4
- 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
- 2024EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learningcited by 4
50 most cited of 87 All 87 (JSON)
- 2026On Gossip Algorithms for Machine Learning with Pairwise Objectives25 Mar 2026
- 2026Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions16 Mar 2026
- 2026First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints6 Mar 2026
- 2026Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins20 Feb 2026
- 2026Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective12 Feb 2026
- 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspective31 Jan 2026
- 2025FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs29 Dec 2025
- 2025DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning12 Dec 2025
- 2025Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity22 Sep 2025
- 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions19 Aug 2025
- 2025Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models23 Jun 2025
- 2025Towards Fundamental Limits for Active Multi-distribution Learning21 Jun 2025
38 more
- 2025FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning4 Jun 2025
- 2025Mitigating Group-Level Fairness Disparities in Federated Visual Language Models3 May 2025
- 2025Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning30 Apr 2025
- 2025The Cost of Local and Global Fairness in Federated Learning27 Mar 2025
- 2025pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning19 Mar 2025
- 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learning18 Jan 2025
- 2025AFed: Algorithmic Fair Federated Learning6 Jan 2025
- 2024Review of Mathematical Optimization in Federated Learning2 Dec 2024
- 2024WassFFed: Wasserstein Fair Federated Learning11 Nov 2024
- 2024A New Perspective to Boost Performance Fairness for Medical Federated Learning12 Oct 2024
- 2024PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning9 Oct 2024
- 2024FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction7 Oct 2024
- 2024Fair Decentralized Learning3 Oct 2024
- 2024EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning2 Oct 2024
- 2024WHOMP: Optimizing Randomized Controlled Trials via Wasserstein Homogeneity27 Sep 2024
- 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learning15 Aug 2024
- 2024Achieving Fairness Across Local and Global Models in Federated Learning24 Jun 2024
- 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives16 Jun 2024
- 2024Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing28 May 2024
- 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning22 May 2024
- 2024CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics30 Apr 2024
- 2024PraFFL: A Preference-Aware Scheme in Fair Federated Learning13 Apr 2024
- 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Direction10 Jan 2024
- 2024GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning7 Jan 2024
- 2023Harnessing the Power of Federated Learning in Federated Contextual Bandits26 Dec 2023
- 2023No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation10 Dec 2023
- 2023Multi-dimensional Fair Federated Learning9 Dec 2023
- 2023Optimal Multi-Distribution Learning8 Dec 2023
- 2023Equipping Federated Graph Neural Networks with Structure-aware Group Fairness18 Oct 2023
- 2023Federated Learning with Convex Global and Local Constraints16 Oct 2023
- 2023Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect28 Sep 2023
- 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devices13 Sep 2023
- 2023Bias Propagation in Federated Learning5 Sep 2023
- 2023Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models26 Aug 2023
- 2023Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition21 Jul 2023
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challenges20 Jul 2023
- 2023Benchmarking Algorithms for Federated Domain Generalization11 Jul 2023
- 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-off25 Jun 2023
50 newest of 87 All 87 (JSON)
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
- 2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
- 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
- 2021Towards Fairness-Aware Federated Learningcited by 57
- 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
- 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
- 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
- 2021Improving Fairness via Federated Learningcited by 39
- 2022Test-Time Robust Personalization for Federated Learningcited by 39
- 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
- 2021FedJAX: Federated learning simulation with JAXcited by 32
- 2021Training Fair Models in Federated Learning without Data Privacy Infringementcited by 26
12 most cited of 86 All 86 (JSON)
- 2026On Gossip Algorithms for Machine Learning with Pairwise Objectives25 Mar 2026
- 2026Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions16 Mar 2026
- 2026First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints6 Mar 2026
- 2026Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins20 Feb 2026
- 2026Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective12 Feb 2026
- 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspective31 Jan 2026
- 2025FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs29 Dec 2025
- 2025DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning12 Dec 2025
- 2025Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity22 Sep 2025
- 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions19 Aug 2025
- 2025Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models23 Jun 2025
- 2025Towards Fundamental Limits for Active Multi-distribution Learning21 Jun 2025
12 newest of 86 All 86 (JSON)