mlsanityData to 14 Oct 2026

FedNoisy: Federated Noisy Label Learning Benchmark

arXiv 2306.11650 ↗First posted 20 Jun 2023cs.LG cs.AIcited by 11

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Abstract (arXiv)

Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients. But meanwhile, the distributed and isolated nature of data isolation may be complicated by data quality, making it more vulnerable to noisy labels. Many efforts exist to defend against the negative impacts of noisy labels in centralized or federated settings. However, there is a lack of a benchmark that comprehensively considers the impact of noisy labels in a wide variety of typical FL settings. In this work, we serve the first standardized benchmark that can help researchers fully explore potential federated noisy settings. Also, we conduct comprehensive experiments to explore the characteristics of these data settings and the comparison across baselines, which may guide method development in the future. We highlight the 20 basic settings for 6 datasets proposed in our benchmark and standardized simulation pipeline for federated noisy label learning, including implementations of 9 baselines. We hope this benchmark can facilitate idea verification in federated learning with noisy labels. textttFedNoisy is available at codewordhttps://github.com/SMILELab-FL/FedNoisy.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 47 of its 77 references that are in the map. First posted on arXiv 20 Jun 2023.

Builds on

47 of 77 references.

References · 47

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35 more
  1. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 582
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  3. 2020Federated Learning with Matched Averagingcited by 564
  4. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  5. 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 500
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  7. 2017Robust Loss Functions under Label Noise for Deep Neural Networkscited by 443
  8. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  9. 2019Unsupervised Label Noise Modeling and Loss Correctioncited by 328
  10. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  11. 2018Joint Optimization Framework for Learning with Noisy Labelscited by 293
  12. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  13. 2017WebVision Database: Visual Learning and Understanding from Web Datacited by 262
  14. 2020Normalized Loss Functions for Deep Learning with Noisy Labelscited by 257
  15. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  16. 2021Federated Learning for Open Bankingcited by 141
  17. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  18. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  19. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  20. 2019Robust Inference via Generative Classifiers for Handling Noisy Labelscited by 68
  21. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  22. 2021FedLab: A Flexible Federated Learning Frameworkcited by 55
  23. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 50
  24. 2017Deep Learning from Noisy Image Labels with Quality Embeddingcited by 46
  25. 2020Robust Federated Learning with Noisy Labelscited by 40
  26. 2020FOCUS: Dealing with Label Quality Disparity in Federated Learningcited by 36
  27. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  28. 2022FLAIR: Federated Learning Annotated Image Repositorycited by 22
  29. 2021Federated Noisy Client Learningcited by 13
  30. 2022FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labelscited by 12
  31. 2022Communication-Efficient Robust Federated Learning with Noisy Labelscited by 11
  32. 2021An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imagingcited by 10
  33. 2023A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detectioncited by 10
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  35. 2021Learning Deep Neural Networks under Agnostic Corrupted Supervisioncited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 6; 2026: 2.

11 papers.

  1. 2023Federated Learning with Extremely Noisy Clients via Negative Distillationcited by 12
  2. 2025FNBench: Benchmarking Robust Federated Learning against Noisy Labelscited by 6
  3. 2024Federated Learning Client Pruning for Noisy Labelscited by 4
  4. 2023Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clientscited by 1
  5. 2025FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learningcited by 1
  6. 2025Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Datacited by 0
  7. 2025WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Siloscited by 0
  8. 2025Whom to Trust? Adaptive Collaboration in Personalized Federated Learningcited by 0
  9. 2025Lightweight and Robust Federated Data Valuationcited by 0
  10. 2026FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clientscited by 0
  11. 2026Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selectioncited by 0