mlsanityData to 14 Oct 2026

FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity

arXiv 2305.05230 ↗First posted 9 May 2023cs.LG cs.AIcited by 33

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Abstract (arXiv)

Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of class-balanced global data, might be incapable to model complicated label noise, especially in medical scenarios. In this paper, we first formulate a new and more realistic federated label noise problem where global data is class-imbalanced and label noise is heterogeneous, and then propose a two-stage framework named FedNoRo for noise-robust federated learning. Specifically, in the first stage of FedNoRo, per-class loss indicators followed by Gaussian Mixture Model are deployed for noisy client identification. In the second stage, knowledge distillation and a distance-aware aggregation function are jointly adopted for noise-robust federated model updating. Experimental results on the widely-used ICH and ISIC2019 datasets demonstrate the superiority of FedNoRo against the state-of-the-art FNLL methods for addressing class imbalance and label noise heterogeneity in real-world FL scenarios.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Builds on 40 of its 65 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

40 of 65 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  7. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  8. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,059
  9. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  10. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 989
  11. 2019Decoupling Representation and Classifier for Long-Tailed Recognitioncited by 809
  12. 2021Model-Contrastive Federated Learningcited by 766
28 more
  1. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 692
  2. 2020Long-tail learning via logit adjustmentcited by 618
  3. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  4. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
  5. 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 500
  6. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  7. 2019BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognitioncited by 403
  8. 2019How does Disagreement Help Generalization against Label Corruption?cited by 390
  9. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  10. 2019BCN20000: Dermoscopic Lesions in the Wildcited by 225
  11. 2020Does label smoothing mitigate label noise?cited by 204
  12. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 158
  13. 2020Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noisecited by 82
  14. 2021To Smooth or Not? When Label Smoothing Meets Noisy Labelscited by 70
  15. 2022Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Featurescited by 63
  16. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  17. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  18. 2020Robust Federated Learning with Noisy Labelscited by 40
  19. 2020FOCUS: Dealing with Label Quality Disparity in Federated Learningcited by 36
  20. 2022Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalizationcited by 33
  21. 2022RSCFed: Random Sampling Consensus Federated Semi-supervised Learningcited by 32
  22. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
  23. 2022Identifying Hard Noise in Long-Tailed Sample Distributioncited by 19
  24. 2021Learning From Long-Tailed Data With Noisy Labelscited by 17
  25. 2022Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labelscited by 16
  26. 2019"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filteringcited by 13
  27. 2022Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalancecited by 13
  28. 2022FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labelscited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 8; 2025: 9; 2026: 10.

33 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  2. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  3. 2024Federated Distillation: A Surveycited by 15
  4. 2023FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noisecited by 14
  5. 2023Federated Learning with Extremely Noisy Clients via Negative Distillationcited by 12
  6. 2024Tackling Noisy Clients in Federated Learning with End-to-end Label Correctioncited by 12
  7. 2023FedNoisy: Federated Noisy Label Learning Benchmarkcited by 11
  8. 2023FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Thingscited by 8
  9. 2025FNBench: Benchmarking Robust Federated Learning against Noisy Labelscited by 6
  10. 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
  11. 2024FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneitycited by 5
  12. 2024FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learningcited by 4
21 more
  1. 2024FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completenesscited by 2
  2. 2025FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysiscited by 2
  3. 2026FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learningcited by 2
  4. 2024On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learningcited by 1
  5. 2026FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labelscited by 1
  6. 2024Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labelscited by 0
  7. 2025Task-Agnostic Federation over Decentralized Data: Research Landscape and Visionscited by 0
  8. 2025Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matchingcited by 0
  9. 2025FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labelscited by 0
  10. 2025Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Datacited by 0
  11. 2025Lightweight and Robust Federated Data Valuationcited by 0
  12. 2025Local K-Similarity Constraint for Federated Learning with Label Noisecited by 0
  13. 2025Single-Round Scalable Analytic Federated Learningcited by 0
  14. 2026FedCova: Robust Federated Covariance Learning Against Noisy Labelscited by 0
  15. 2026FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clientscited by 0
  16. 2026FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocationcited by 0
  17. 2026Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selectioncited by 0
  18. 2026Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learningcited by 0
  19. 2026One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classificationcited by 0
  20. 2026Label Granularity Skew in Federated Learning with Hierarchical Image Classificationcited by 0
  21. 2026OCT-FedSIR: Toward Trustworthy Federated Ophthalmic Learning under Annotation Noisecited by 0

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  2. 2024Exploring Vacant Classes in Label-Skewed Federated Learning
  3. 2024Upcycling Noise for Federated Unlearning
  4. 2026CALM: Class-wise Agreement and Label-gated Disagreement Modulation for Decentralized Federated Learning
  5. 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
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  7. 2026FedLTLib: A Comprehensive Benchmark for Federated Long-Tail Learning
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