Robust Federated Learning with Noisy Labels

IEEE Intelligent Systems (IEEE Intell. Syst.), 2020

3 December 2020

Papers citing "Robust Federated Learning with Noisy Labels"

31 / 31 papers shown

Poison to Detect: Detection of Targeted Overfitting in Federated Learning

Soumia Zohra El Mestari

Maciej Krzysztof Zuziak

Gabriele Lenzini

159

15 Sep 2025

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data

Alpaslan Gokcen

Ali Boyaci

FedML

367

14 May 2025

FNBench: Benchmarking Robust Federated Learning against Noisy Labels

313

10 May 2025

FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels

317

08 Apr 2025

Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions

Keqin Li

475

05 Mar 2025

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels

363

30 Nov 2024

Data Quality Control in Federated Instruction-tuning of Large Language Models

508

15 Oct 2024

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness

273

23 Jul 2024

Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification

310

17 Jun 2024

Advances in Robust Federated Learning: A Survey with Heterogeneity ConsiderationsIEEE Transactions on Big Data (IEEE Trans. Big Data), 2024

427

16 May 2024

Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data DomainsNeural Information Processing Systems (NeurIPS), 2024

236

14 Mar 2024

Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels

Taehyeon Kim

Donggyu Kim

SeYoung Yun

152

08 Feb 2024

Federated Learning with Instance-Dependent Noisy LabelIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023

312

16 Dec 2023

Personalized Federated Learning via ADMM with Moreau Envelope

243

12 Nov 2023

Heterogeneous Federated Learning: State-of-the-art and Research ChallengesACM Computing Surveys (ACM Comput. Surv.), 2023

Bo Du

511

540

20 Jul 2023

FedNoisy: Federated Noisy Label Learning Benchmark

568

20 Jun 2023

FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise HeterogeneityInternational Joint Conference on Artificial Intelligence (IJCAI), 2023

320

09 May 2023

Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate AnnotationsIEEE International Symposium on Biomedical Imaging (ISBI), 2023

Zahra Hafezi Kafshgari

220

28 Apr 2023

Learning Cautiously in Federated Learning with Noisy and Heterogeneous ClientsIEEE International Conference on Multimedia and Expo (ICME), 2023

161

06 Apr 2023

Quantifying the Impact of Label Noise on Federated Learning

Shuqi Ke

Chao Huang

Xin Liu

FedML

445

15 Nov 2022

Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning

225

03 Sep 2022

Towards Federated Learning against Noisy Labels via Local Self-RegularizationInternational Conference on Information and Knowledge Management (CIKM), 2022

237

25 Aug 2022

Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsACM Transactions on Intelligent Systems and Technology (ACM TIST), 2022

254

19 Aug 2022

Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets

106

23 Jun 2022

Communication-Efficient Robust Federated Learning with Noisy LabelsKnowledge Discovery and Data Mining (KDD), 2022

228

11 Jun 2022

FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels

338

20 May 2022

FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated LearningInternational Conference on Information and Knowledge Management (CIKM), 2022

325

03 May 2022

FedCorr: Multi-Stage Federated Learning for Label Noise CorrectionComputer Vision and Pattern Recognition (CVPR), 2022

Jingyi Xu

Zihan Chen

Tony Q.S. Quek

Kai Fong Ernest Chong

FedML

320

127

10 Apr 2022

Federated Noisy Client Learning

Huazhu Fu

Li Li

Bo Han

Chengzhong Xu

Ling Shao

FedML

272

24 Jun 2021

FINE Samples for Learning with Noisy LabelsNeural Information Processing Systems (NeurIPS), 2021

375

130

23 Feb 2021

Combating noisy labels by agreement: A joint training method with co-regularizationComputer Vision and Pattern Recognition (CVPR), 2020

1.0K

659

05 Mar 2020