mlsanityData to 14 Oct 2026

AFed: Algorithmic Fair Federated Learning

arXiv 2501.02732 ↗First posted 6 Jan 2025cs.LG cs.AIcited by 2

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Abstract (arXiv)

Federated Learning (FL) has gained significant attention as it facilitates collaborative machine learning among multiple clients without centralizing their data on a server. FL ensures the privacy of participating clients by locally storing their data, which creates new challenges in fairness. Traditional debiasing methods assume centralized access to sensitive information, rendering them impractical for the FL setting. Additionally, FL is more susceptible to fairness issues than centralized machine learning due to the diverse client data sources that may be associated with group information. Therefore, training a fair model in FL without access to client local data is important and challenging. This paper presents AFed, a straightforward yet effective framework for promoting group fairness in FL. The core idea is to circumvent restricted data access by learning the global data distribution. This paper proposes two approaches: AFed-G, which uses a conditional generator trained on the server side, and AFed-GAN, which improves upon AFed-G by training a conditional GAN on the client side. We augment the client data with the generated samples to help remove bias. Our theoretical analysis justifies the proposed methods, and empirical results on multiple real-world datasets demonstrate a substantial improvement in AFed over several baselines.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 37 of its 59 references that are in the map. First posted on arXiv 6 Jan 2025.

Builds on

37 of 59 references.

References · 37

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25 more
  1. 2019Agnostic Federated Learningcited by 456
  2. 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 429
  3. 2019Fair Resource Allocation in Federated Learningcited by 394
  4. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
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  22. 2020Fairness and Accuracy in Federated Learningcited by 27
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  25. 2023Mitigating Group Bias in Federated Learning: Beyond Local Fairnesscited by 10

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMscited by 8
  2. 2025FAIRVAR: Fair Federated Learning via Variance Regularizationcited by 0