mlsanityData to 14 Oct 2026

PraFFL: A Preference-Aware Scheme in Fair Federated Learning

arXiv 2404.08973 ↗First posted 13 Apr 2024cs.LG cs.CY cs.DCcited by 5

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Abstract (arXiv)

Fairness in federated learning has emerged as a critical concern, aiming to develop an unbiased model among groups (e.g., male or female) of diverse sensitive features. However, there is a trade-off between model performance and fairness, i.e., improving model fairness will decrease model performance. Existing approaches have characterized such a trade-off by introducing hyperparameters to quantify client's preferences for model fairness and model performance. Nevertheless, these approaches are limited to scenarios where each client has only a single pre-defined preference, and fail to work in practical systems where each client generally has multiple preferences. To this end, we propose a Preference-aware scheme in Fair Federated Learning (called PraFFL) to generate preference-specific models in real time. PraFFL can adaptively adjust the model based on each client's preferences to meet their needs. We theoretically prove that PraFFL can offer the optimal model tailored to an arbitrary preference of each client, and show its linear convergence. Experimental results show that our proposed PraFFL outperforms six fair federated learning algorithms in terms of the model's capability of adapting to clients' different preferences. Our implementation is available at https://github.com/rG223/PraFFL.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 32 of its 49 references that are in the map. First posted on arXiv 13 Apr 2024.

Builds on

32 of 49 references.

References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2021Convex optimizationcited by 2,515
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  6. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  7. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  8. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  9. 2021Towards Personalized Federated Learningcited by 464
  10. 2019Agnostic Federated Learningcited by 456
  11. 2019Fair Resource Allocation in Federated Learningcited by 394
  12. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
20 more
  1. 2021Distributionally Robust Federated Averagingcited by 104
  2. 2020FairBatch: Batch Selection for Model Fairnesscited by 99
  3. 2020Collaborative Fairness in Federated Learningcited by 95
  4. 2020Fairness-aware Agnostic Federated Learningcited by 87
  5. 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
  6. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  7. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  8. 2021Federated Learning with Fair Averagingcited by 61
  9. 2020Mitigating Bias in Federated Learningcited by 56
  10. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  11. 2021Improving Fairness via Federated Learningcited by 39
  12. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  13. 2019ProPublica's COMPAS Data Revisitedcited by 24
  14. 2022Minimax Demographic Group Fairness in Federated Learningcited by 24
  15. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
  16. 2022Fair Federated Learning via Bounded Group Losscited by 12
  17. 2022FAIR-FATE: Fair Federated Learning with Momentumcited by 10
  18. 2023Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decompositioncited by 10
  19. 2022Towards Multi-Objective Statistically Fair Federated Learningcited by 8
  20. 2024Federated Fairness without Access to Sensitive Groupscited by 2

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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Trainingcited by 10
  2. 2024Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Drivingcited by 8
  3. 2024Pareto Front Shape-Agnostic Pareto Set Learning in Multi-Objective Optimizationcited by 0
  4. 2025Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learningcited by 0
  5. 2025FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learningcited by 0