# Robust Federated Learning with Noisy Labels

arXiv: https://arxiv.org/abs/2012.01700 · HTML: https://mlsanity.com/paper/2012.01700/

## Key facts

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

## Built on by (most cited first, 30 of 40)

- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [FINE Samples for Learning with Noisy Labels](https://mlsanity.com/paper/2102.11628/) (2021)
- [FedCorr: Multi-Stage Federated Learning for Label Noise Correction](https://mlsanity.com/paper/2204.04677/) (2022)
- [FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity](https://mlsanity.com/paper/2305.05230/) (2023)
- [Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains](https://mlsanity.com/paper/2403.09048/) (2024)
- [Towards Federated Learning against Noisy Labels via Local Self-Regularization](https://mlsanity.com/paper/2208.12807/) (2022)
- [Federated Noisy Client Learning](https://mlsanity.com/paper/2106.13239/) (2021)
- [Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels](https://mlsanity.com/paper/2208.09378/) (2022)
- [FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels](https://mlsanity.com/paper/2205.10110/) (2022)
- [Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients](https://mlsanity.com/paper/2304.02892/) (2023)
- [Communication-Efficient Robust Federated Learning with Noisy Labels](https://mlsanity.com/paper/2206.05558/) (2022)
- [FedNoisy: Federated Noisy Label Learning Benchmark](https://mlsanity.com/paper/2306.11650/) (2023)
- [Federated Learning with Instance-Dependent Noisy Label](https://mlsanity.com/paper/2312.10324/) (2023)
- [FNBench: Benchmarking Robust Federated Learning against Noisy Labels](https://mlsanity.com/paper/2505.06684/) (2025)
- [Entropy-driven Fair and Effective Federated Learning](https://mlsanity.com/paper/2301.12407/) (2023)
- [Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations](https://mlsanity.com/paper/2304.14976/) (2023)
- [Feature Diversification and Adaptation for Federated Domain Generalization](https://mlsanity.com/paper/2407.08245/) (2024)
- [Quantifying the Impact of Label Noise on Federated Learning](https://mlsanity.com/paper/2211.07816/) (2022)
- [FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning](https://mlsanity.com/paper/2205.01310/) (2022)
- [Collaboratively Learning Federated Models from Noisy Decentralized Data](https://mlsanity.com/paper/2409.02189/) (2024)
- [Federated Learning from Molecules to Processes: A Perspective](https://mlsanity.com/paper/2506.18525/) (2025)
- [FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning](https://mlsanity.com/paper/2604.19729/) (2026)
- [Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning](https://mlsanity.com/paper/2209.01417/) (2022)
- [Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels](https://mlsanity.com/paper/2402.05353/) (2024)
- [Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification](https://mlsanity.com/paper/2406.11310/) (2024)
- [Data Quality Control in Federated Instruction-tuning of Large Language Models](https://mlsanity.com/paper/2410.11540/) (2024)
- [FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels](https://mlsanity.com/paper/2604.20825/) (2026)
- [Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets](https://mlsanity.com/paper/2206.11715/) (2022)
- [Personalized Federated Learning via ADMM with Moreau Envelope](https://mlsanity.com/paper/2311.06756/) (2023)
- [On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness](https://mlsanity.com/paper/2407.16397/) (2024)

## 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.