mlsanityData to 14 Oct 2026

Fairness and Accuracy in Federated Learning

arXiv 2012.10069 ↗First posted 18 Dec 2020cs.LGcited by 27

Not on the map. Why?

Abstract (arXiv)

In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy. Normally, inconsistent distribution of data across different devices in a federated network and limited communication bandwidth between end devices impose both statistical heterogeneity and expensive communication as major challenges for federated learning. This paper proposes an algorithm to achieve more fairness and accuracy in federated learning (FedFa). It introduces an optimization scheme that employs a double momentum gradient, thereby accelerating the convergence rate of the model. An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed. This procedure assists in addressing the issue of unfairness in federated learning due to preferences for certain clients. Our results show that the proposed FedFa algorithm outperforms the baseline algorithm in terms of accuracy and fairness.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Dec 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 7; 2022: 5; 2023: 4; 2024: 7; 2025: 4.

27 papers.

Sort:

By area:

  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  2. 2021Federated Learning with Fair Averagingcited by 61
  3. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  4. 2021FedJAX: Federated learning simulation with JAXcited by 32
  5. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  6. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  7. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
  8. 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 13
  9. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  10. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 9
  11. 2022FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learningcited by 7
  12. 2022Incentivizing Federated Learningcited by 5
15 more
  1. 2023CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularizationcited by 5
  2. 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 5
  3. 2023Fairness and Privacy in Federated Learning and Their Implications in Healthcarecited by 3
  4. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  5. 2022Fairness in Federated Learning for Spatial-Temporal Applicationscited by 2
  6. 2022A Survey on Preserving Fairness Guarantees in Changing Environmentscited by 2
  7. 2024How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learningcited by 2
  8. 2025AFed: Algorithmic Fair Federated Learningcited by 2
  9. 2025Achieving Distributive Justice in Federated Learning via Uncertainty Quantificationcited by 2
  10. 2021Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasetscited by 1
  11. 2021Demystifying the Effects of Non-Independence in Federated Learningcited by 1
  12. 2024Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clusteringcited by 1
  13. 2024Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcarecited by 0
  14. 2024RFID based Health Adherence Medicine Case Using Fair Federated Learningcited by 0
  15. 2025CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learningcited by 0