mlsanityData to 7 Oct 2026

Robust Federated Learning with Noisy Labels

arXiv 2012.01700 ↗First posted 3 Dec 2020cs.LG cs.CVcited by 40

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Abstract (arXiv)

Federated learning is a paradigm that enables local devices to jointly train a server model while keeping the data decentralized and private. In federated learning, since local data are collected by clients, it is hardly guaranteed that the data are correctly annotated. Although a lot of studies have been conducted to train the networks robust to these noisy data in a centralized setting, these algorithms still suffer from noisy labels in federated learning. Compared to the centralized setting, clients' data can have different noise distributions due to variations in their labeling systems or background knowledge of users. As a result, local models form inconsistent decision boundaries and their weights severely diverge from each other, which are serious problems in federated learning. To solve these problems, we introduce a novel federated learning scheme that the server cooperates with local models to maintain consistent decision boundaries by interchanging class-wise centroids. These centroids are central features of local data on each device, which are aligned by the server every communication round. Updating local models with the aligned centroids helps to form consistent decision boundaries among local models, although the noise distributions in clients' data are different from each other. To improve local model performance, we introduce a novel approach to select confident samples that are used for updating the model with given labels. Furthermore, we propose a global-guided pseudo-labeling method to update labels of unconfident samples by exploiting the global model. Our experimental results on the noisy CIFAR-10 dataset and the Clothing1M dataset show that our approach is noticeably effective in federated learning with noisy labels.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 9; 2023: 8; 2024: 8; 2025: 6; 2026: 7.

40 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2021FINE Samples for Learning with Noisy Labelscited by 82
  3. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  4. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  5. 2024Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domainscited by 27
  6. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
  7. 2021Federated Noisy Client Learningcited by 13
  8. 2022Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labelscited by 13
  9. 2022FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labelscited by 12
  10. 2023Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clientscited by 12
  11. 2022Communication-Efficient Robust Federated Learning with Noisy Labelscited by 11
  12. 2023FedNoisy: Federated Noisy Label Learning Benchmarkcited by 11
28 more
  1. 2023Federated Learning with Instance-Dependent Noisy Labelcited by 9
  2. 2025FNBench: Benchmarking Robust Federated Learning against Noisy Labelscited by 6
  3. 2023Entropy-driven Fair and Effective Federated Learningcited by 4
  4. 2023Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotationscited by 4
  5. 2024Feature Diversification and Adaptation for Federated Domain Generalizationcited by 4
  6. 2022Quantifying the Impact of Label Noise on Federated Learningcited by 3
  7. 2022FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learningcited by 2
  8. 2024Collaboratively Learning Federated Models from Noisy Decentralized Datacited by 2
  9. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 2
  10. 2026FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learningcited by 2
  11. 2022Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learningcited by 1
  12. 2024Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labelscited by 1
  13. 2024Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classificationcited by 1
  14. 2024Data Quality Control in Federated Instruction-tuning of Large Language Modelscited by 1
  15. 2026FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labelscited by 1
  16. 2022Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Marketscited by 0
  17. 2023Personalized Federated Learning via ADMM with Moreau Envelopecited by 0
  18. 2024On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairnesscited by 0
  19. 2024Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labelscited by 0
  20. 2025FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labelscited by 0
  21. 2025Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Datacited by 0
  22. 2025Poison to Detect: Detection of Targeted Overfitting in Federated Learningcited by 0
  23. 2025Local K-Similarity Constraint for Federated Learning with Label Noisecited by 0
  24. 2026Smart Split-Federated Learning over Noisy Channels for Embryo Image Segmentationcited by 0
  25. 2026FedCova: Robust Federated Covariance Learning Against Noisy Labelscited by 0
  26. 2026Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selectioncited by 0
  27. 2026Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learningcited by 0
  28. 2026OCT-FedSIR: Toward Trustworthy Federated Ophthalmic Learning under Annotation Noisecited by 0