Data to 7 Oct 2026

GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning

arXiv 2108.02741 ↗First posted 5 Aug 2021cs.LG cs.DCcited by 21

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Abstract (arXiv)

In this paper we propose textttGIFAIR-FL: a framework that imposes Group and Individual FAIRness to Federated Learning settings. By adding a regularization term, our algorithm penalizes the spread in the loss of client groups to drive the optimizer to fair solutions. Our framework textttGIFAIR-FL can accommodate both global and personalized settings. Theoretically, we show convergence in non-convex and strongly convex settings. Our convergence guarantees hold for both i.i.d. and non-i.i.d. data. To demonstrate the empirical performance of our algorithm, we apply our method to image classification and text prediction tasks. Compared to existing algorithms, our method shows improved fairness results while retaining superior or similar prediction accuracy.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 39 of its 61 references that are in the map. First posted on arXiv 5 Aug 2021.

Builds on

39 of 61 references.

References · 39

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2019Advances and Open Problems in Federated Learningcited by 3,021
2018Federated Optimization in Heterogeneous Networkscited by 2,971
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2020Adaptive Federated Optimizationcited by 1,036
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2014Certifying and removing disparate impactcited by 715
2017Federated Multi-Task Learningcited by 669
2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
2020Personalized Federated Learning with Moreau Envelopescited by 612

27 more

2018Federated Learning for Mobile Keyboard Predictioncited by 565
2020Federated Learning with Matched Averagingcited by 564
2019Federated Learning with Personalization Layerscited by 533
2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
2019Agnostic Federated Learningcited by 451
2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
2019Fair Resource Allocation in Federated Learningcited by 389
2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
2018Analyzing Federated Learning through an Adversarial Lenscited by 346
2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
2018Federated Learning via Over-the-Air Computationcited by 215
2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
2019Federated Evaluation of On-device Personalizationcited by 147
2020Salvaging Federated Learning by Local Adaptationcited by 142
2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 115
2020Tilted Empirical Risk Minimizationcited by 103
2020Collaborative Fairness in Federated Learningcited by 95
2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
2020Fairness-aware Agnostic Federated Learningcited by 87
2020Federated Accelerated Stochastic Gradient Descentcited by 85
2020Federated Bayesian Optimization via Thompson Samplingcited by 77
2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
2020Hierarchically Fair Federated Learningcited by 28
2020Fairness and Accuracy in Federated Learningcited by 27

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 6; 2024: 7; 2025: 4; 2026: 1.

21 papers.

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2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
2022Proportional Fairness in Federated Learningcited by 20
2022Fair Federated Learning via Bounded Group Losscited by 12
2023Mitigating Group Bias in Federated Learning: Beyond Local Fairnesscited by 10
2022Federated Data Analytics: A Study on Linear Modelscited by 9
2023Personalized Dictionary Learning for Heterogeneous Datasetscited 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
2024Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspectivecited by 5
2023Multi-dimensional Fair Federated Learningcited by 4
2024Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processingcited by 4

9 more

2024EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learningcited by 4
2024Information-Geometric Barycenters for Bayesian Federated Learningcited by 1
2025Global Group Fairness in Federated Learning via Function Trackingcited by 1
2026Collaborative Contextual Bayesian Optimizationcited by 1
2023Echoes of Biases: How Stigmatizing Language Affects AI Performancecited by 0
2024Equitable Federated Learning with Activation Clusteringcited by 0
2025Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learningcited by 0
2025FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networkscited by 0
2025DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learningcited by 0