mlsanityData to 14 Oct 2026

E2FL: Equal and Equitable Federated Learning

arXiv 2205.10454 ↗First posted 20 May 2022cs.LGcited by 3

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Abstract (arXiv)

Federated Learning (FL) enables data owners to train a shared global model without sharing their private data. Unfortunately, FL is susceptible to an intrinsic fairness issue: due to heterogeneity in clients' data distributions, the final trained model can give disproportionate advantages across the participating clients. In this work, we present Equal and Equitable Federated Learning (E2FL) to produce fair federated learning models by preserving two main fairness properties, equity and equality, concurrently. We validate the efficiency and fairness of E2FL in different real-world FL applications, and show that E2FL outperforms existing baselines in terms of the resulting efficiency, fairness of different groups, and fairness among all individual clients.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 38 of its 47 references that are in the map. First posted on arXiv 20 May 2022.

Builds on

38 of 47 references.

References · 38

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26 more
  1. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  2. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  3. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  4. 2019Agnostic Federated Learningcited by 456
  5. 2019Fair Resource Allocation in Federated Learningcited by 394
  6. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 364
  7. 2020Adaptive Personalized Federated Learningcited by 358
  8. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  9. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
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  16. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
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  20. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
  21. 2020Tilted Empirical Risk Minimizationcited by 106
  22. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 85
  23. 2020LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasetscited by 60
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  26. 2013Big Neural Networks Waste Capacitycited by 10

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

  1. 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
  2. 2025Achieving Distributive Justice in Federated Learning via Uncertainty Quantificationcited by 2
  3. 2025FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networkscited by 0