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Unified Group Fairness on Federated Learning

arXiv 2111.04986 ↗First posted 9 Nov 2021cs.LGcited by 15

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Abstract (arXiv)

Federated learning (FL) has emerged as an important machine learning paradigm where a global model is trained based on the private data from distributed clients. However, most of existing FL algorithms cannot guarantee the performance fairness towards different groups because of data distribution shift over groups. In this paper, we formulate the problem of unified group fairness on FL, where the groups can be formed by clients (including existing clients and newly added clients) and sensitive attribute(s). To solve this problem, we first propose a general fair federated framework. Then we construct a unified group fairness risk from the view of federated uncertainty set with theoretical analyses to guarantee unified group fairness on FL. We also develop an efficient federated optimization algorithm named Federated Mirror Descent Ascent with Momentum Acceleration (FMDA-M) with convergence guarantee. We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 39 of its 59 references that are in the map. First posted on arXiv 9 Nov 2021.

Builds on

39 of 59 references.

References · 39

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  12. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
27 more
  1. 2019Agnostic Federated Learningcited by 456
  2. 2019Fair Resource Allocation in Federated Learningcited by 394
  3. 2020Adaptive Personalized Federated Learningcited by 358
  4. 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 337
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 2; 2024: 4; 2025: 1; 2026: 1.

15 papers.

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  1. 2022DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalizationcited by 35
  2. 2022Minimax Demographic Group Fairness in Federated Learningcited by 24
  3. 2022Fairness in Federated Learning via Core-Stabilitycited by 23
  4. 2022Local Stochastic Bilevel Optimization with Momentum-Based Variance Reductioncited by 20
  5. 2022PrivFairFL: Privacy-Preserving Group Fairness in Federated Learningcited by 13
  6. 2022Fair Federated Learning via Bounded Group Losscited by 12
  7. 2024Addressing Polarization and Unfairness in Performative Predictioncited by 11
  8. 2021Models of fairness in federated learningcited by 8
  9. 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
  10. 2023FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learningcited by 5
  11. 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
  12. 2024Federated Fairness without Access to Sensitive Groupscited by 2
3 more
  1. 2025Leveraging Machine Unlearning for Cost-Efficient Preference Alignmentcited by 2
  2. 2024Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clusteringcited by 1
  3. 2026When the Server Steps In: Calibrated Updates for Fair Federated Learningcited by 0