mlsanityData to 7 Oct 2026

Fair Federated Learning via Bounded Group Loss

arXiv 2203.10190 ↗First posted 18 Mar 2022cs.LG cs.CYcited by 12

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Abstract (arXiv)

Fair prediction across protected groups is an important constraint for many federated learning applications. However, prior work studying group fair federated learning lacks formal convergence or fairness guarantees. In this work we propose a general framework for provably fair federated learning. In particular, we explore and extend the notion of Bounded Group Loss as a theoretically-grounded approach for group fairness. Using this setup, we propose a scalable federated optimization method that optimizes the empirical risk under a number of group fairness constraints. We provide convergence guarantees for the method as well as fairness guarantees for the resulting solution. Empirically, we evaluate our method across common benchmarks from fair ML and federated learning, showing that it can provide both fairer and more accurate predictions than baseline approaches.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 32 of its 53 references that are in the map. First posted on arXiv 18 Mar 2022.

Builds on

32 of 53 references.

References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2016Deep Learning with Differential Privacycited by 2,682
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2018LEAF: A Benchmark for Federated Settingscited by 780
  9. 2014Certifying and removing disparate impactcited by 715
  10. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  11. 2018A Reductions Approach to Fair Classificationcited by 585
  12. 2019Agnostic Federated Learningcited by 451
20 more
  1. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  2. 2019Fair Resource Allocation in Federated Learningcited by 389
  3. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  4. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  5. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  6. 2020Minimax Pareto Fairness: A Multi Objective Perspectivecited by 152
  7. 2019Federated Adversarial Domain Adaptationcited by 140
  8. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
  9. 2020FairBatch: Batch Selection for Model Fairnesscited by 99
  10. 2020Fairness-aware Agnostic Federated Learningcited by 87
  11. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  12. 2021Improving Fairness via Federated Learningcited by 39
  13. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  14. 2021Training Fair Models in Federated Learning without Data Privacy Infringementcited by 26
  15. 2022Minimax Demographic Group Fairness in Federated Learningcited by 24
  16. 2022Differentially Private SGDA for Minimax Problemscited by 23
  17. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
  18. 2021Efficient Algorithms for Federated Saddle Point Optimizationcited by 20
  19. 2021Unified Group Fairness on Federated Learningcited by 15
  20. 2021FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptationcited by 9

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Cited by year

Citing papers per year: 2023: 4; 2024: 7; 2025: 1.

12 papers.

  1. 2023Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methodscited by 17
  2. 2023Mitigating Group Bias in Federated Learning: Beyond Local Fairnesscited by 10
  3. 2024Federated Unlearning: a Perspective of Stability and Fairnesscited by 7
  4. 2024PraFFL: A Preference-Aware Scheme in Fair Federated Learningcited by 5
  5. 2023Fairness and Privacy in Federated Learning and Their Implications in Healthcarecited by 3
  6. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  7. 2024Federated Fairness without Access to Sensitive Groupscited by 2
  8. 2024PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learningcited by 1
  9. 2025Global Group Fairness in Federated Learning via Function Trackingcited by 1
  10. 2023Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effectcited by 0
  11. 2024Distribution-Free Fair Federated Learning with Small Samplescited by 0
  12. 2024Towards Fairness in Provably Communication-Efficient Federated Recommender Systemscited by 0