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

arXiv: https://arxiv.org/abs/2305.05230 · HTML: https://mlsanity.com/paper/2305.05230/

## 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 (most cited first, 30 of 40)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)
- [How does Disagreement Help Generalization against Label Corruption?](https://mlsanity.com/paper/1901.04215/) (2019)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [BCN20000: Dermoscopic Lesions in the Wild](https://mlsanity.com/paper/1908.02288/) (2019)
- [Does label smoothing mitigate label noise?](https://mlsanity.com/paper/2003.02819/) (2020)
- [cpSGD: Communication-efficient and differentially-private distributed SGD](https://mlsanity.com/paper/1805.10559/) (2018)
- [Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise](https://mlsanity.com/paper/2012.05458/) (2020)
- [To Smooth or Not? When Label Smoothing Meets Noisy Labels](https://mlsanity.com/paper/2106.04149/) (2021)
- [Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features](https://mlsanity.com/paper/2204.13399/) (2022)
- [FedCorr: Multi-Stage Federated Learning for Label Noise Correction](https://mlsanity.com/paper/2204.04677/) (2022)
- [Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching](https://mlsanity.com/paper/2106.08600/) (2021)
- [Robust Federated Learning with Noisy Labels](https://mlsanity.com/paper/2012.01700/) (2020)

## Built on by (most cited first, 30 of 33)

- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise](https://mlsanity.com/paper/2312.12838/) (2023)
- [Federated Learning with Extremely Noisy Clients via Negative Distillation](https://mlsanity.com/paper/2312.12703/) (2023)
- [Tackling Noisy Clients in Federated Learning with End-to-end Label Correction](https://mlsanity.com/paper/2408.04301/) (2024)
- [FedNoisy: Federated Noisy Label Learning Benchmark](https://mlsanity.com/paper/2306.11650/) (2023)
- [FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things](https://mlsanity.com/paper/2310.00109/) (2023)
- [FNBench: Benchmarking Robust Federated Learning against Noisy Labels](https://mlsanity.com/paper/2505.06684/) (2025)
- [From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching](https://mlsanity.com/paper/2404.17805/) (2024)
- [FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity](https://mlsanity.com/paper/2406.18995/) (2024)
- [FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning](https://mlsanity.com/paper/2409.12105/) (2024)
- [FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness](https://mlsanity.com/paper/2407.02280/) (2024)
- [FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis](https://mlsanity.com/paper/2503.10567/) (2025)
- [FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning](https://mlsanity.com/paper/2604.19729/) (2026)
- [On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning](https://mlsanity.com/paper/2410.21192/) (2024)
- [FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels](https://mlsanity.com/paper/2604.20825/) (2026)
- [Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels](https://mlsanity.com/paper/2412.00452/) (2024)
- [Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions](https://mlsanity.com/paper/2503.03140/) (2025)
- [Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching](https://mlsanity.com/paper/2503.09587/) (2025)
- [FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels](https://mlsanity.com/paper/2504.05615/) (2025)
- [Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data](https://mlsanity.com/paper/2505.09733/) (2025)
- [Lightweight and Robust Federated Data Valuation](https://mlsanity.com/paper/2509.25560/) (2025)
- [Local K-Similarity Constraint for Federated Learning with Label Noise](https://mlsanity.com/paper/2511.06169/) (2025)
- [Single-Round Scalable Analytic Federated Learning](https://mlsanity.com/paper/2512.03336/) (2025)
- [FedCova: Robust Federated Covariance Learning Against Noisy Labels](https://mlsanity.com/paper/2603.04062/) (2026)
- [FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients](https://mlsanity.com/paper/2603.19722/) (2026)
- [FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation](https://mlsanity.com/paper/2603.28455/) (2026)
- [Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection](https://mlsanity.com/paper/2606.16868/) (2026)
- [Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning](https://mlsanity.com/paper/2607.01474/) (2026)

## 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.