mlsanityData to 7 Oct 2026

FOCUS: Dealing with Label Quality Disparity in Federated Learning

arXiv 2001.11359 ↗First posted 29 Jan 2020cs.LG stat.MLcited by 36

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Abstract (arXiv)

Ubiquitous systems with End-Edge-Cloud architecture are increasingly being used in healthcare applications. Federated Learning (FL) is highly useful for such applications, due to silo effect and privacy preserving. Existing FL approaches generally do not account for disparities in the quality of local data labels. However, the clients in ubiquitous systems tend to suffer from label noise due to varying skill-levels, biases or malicious tampering of the annotators. In this paper, we propose Federated Opportunistic Computing for Ubiquitous Systems (FOCUS) to address this challenge. It maintains a small set of benchmark samples on the FL server and quantifies the credibility of the client local data without directly observing them by computing the mutual cross-entropy between performance of the FL model on the local datasets and that of the client local FL model on the benchmark dataset. Then, a credit weighted orchestration is performed to adjust the weight assigned to clients in the FL model based on their credibility values. FOCUS has been experimentally evaluated on both synthetic data and real-world data. The results show that it effectively identifies clients with noisy labels and reduces their impact on the model performance, thereby significantly outperforming existing FL approaches.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 11; 2023: 7; 2024: 8; 2025: 4; 2026: 1.

36 papers.

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  1. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 73
  2. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  3. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  4. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  5. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
  6. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  7. 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
  8. 2021Federated Noisy Client Learningcited by 13
  9. 2022Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labelscited by 13
  10. 2022FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labelscited by 12
  11. 2023Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clientscited by 12
  12. 2023Federated Learning with Extremely Noisy Clients via Negative Distillationcited by 12
24 more
  1. 2022Communication-Efficient Robust Federated Learning with Noisy Labelscited by 11
  2. 2023FedNoisy: Federated Noisy Label Learning Benchmarkcited by 11
  3. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  4. 2024Tackling Noisy Clients in Federated Learning with End-to-end Label Correctioncited by 11
  5. 2021Enabling SQL-based Training Data Debugging for Federated Learningcited by 8
  6. 2023A Survey of Federated Evaluation in Federated Learningcited by 7
  7. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  8. 2025FNBench: Benchmarking Robust Federated Learning against Noisy Labelscited by 6
  9. 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
  10. 2024Federated Learning Client Pruning for Noisy Labelscited by 4
  11. 2022Quantifying the Impact of Label Noise on Federated Learningcited by 3
  12. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  13. 2021Robust Convergence in Federated Learning through Label-wise Clusteringcited by 2
  14. 2022FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learningcited by 2
  15. 2022FedFOR: Stateless Heterogeneous Federated Learning with First-Order Regularizationcited by 2
  16. 2024SignSGD with Federated Votingcited by 2
  17. 2024Collaboratively Learning Federated Models from Noisy Decentralized Datacited by 2
  18. 2022Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learningcited by 1
  19. 2024Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labelscited by 1
  20. 2022Deep Unfolding-based Weighted Averaging for Federated Learning in Heterogeneous Environmentscited by 0
  21. 2024FLASH: Federated Learning Across Simultaneous Heterogeneitiescited by 0
  22. 2024Data Quality in Edge Machine Learning: A State-of-the-Art Surveycited by 0
  23. 2025Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clientscited by 0
  24. 2026Knowledge-Free Correlated Agreement for Incentivizing Federated Learningcited by 0