mlsanityData to 14 Oct 2026

Federated Learning Client Pruning for Noisy Labels

arXiv 2411.07391 ↗First posted 11 Nov 2024cs.LG cs.AI cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Federated Learning (FL) enables collaborative model training across decentralized edge devices while preserving data privacy. However, existing FL methods often assume clean annotated datasets, impractical for resource-constrained edge devices. In reality, noisy labels are prevalent, posing significant challenges to FL performance. Prior approaches attempt label correction and robust training techniques but exhibit limited efficacy, particularly under high noise levels. This paper introduces ClipFL (Federated Learning Client Pruning), a novel framework addressing noisy labels from a fresh perspective. ClipFL identifies and excludes noisy clients based on their performance on a clean validation dataset, tracked using a Noise Candidacy Score (NCS). The framework comprises three phases: pre-client pruning to identify potential noisy clients and calculate their NCS, client pruning to exclude a percentage of clients with the highest NCS, and post-client pruning for fine-tuning the global model with standard FL on clean clients. Empirical evaluation demonstrates ClipFL's efficacy across diverse datasets and noise levels, achieving accurate noisy client identification, superior performance, faster convergence, and reduced communication costs compared to state-of-the-art FL methods. Our code is available at https://github.com/MMorafah/ClipFL.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 38 of its 57 references that are in the map. First posted on arXiv 11 Nov 2024.

Builds on

38 of 57 references.

References · 38

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  5. 2019Advances and Open Problems in Federated Learningcited by 3,046
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  7. 2018Federated Learning with Non-IID Datacited by 1,108
  8. 2020Adaptive Federated Optimizationcited by 1,046
  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  10. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  11. 2021Model-Contrastive Federated Learningcited by 766
  12. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
26 more
  1. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  2. 2021How to train your ViT? Data, Augmentation, and Regularization in Vision Transformerscited by 569
  3. 2020Federated Learning with Matched Averagingcited by 564
  4. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  5. 2020Combating noisy labels by agreement: A joint training method with co-regularizationcited by 289
  6. 2022A Survey on Generative Diffusion Modelcited by 240
  7. 2019Model Fusion via Optimal Transportcited by 219
  8. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 154
  9. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 153
  10. 2020A Survey of Label-noise Representation Learning: Past, Present and Futurecited by 122
  11. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  12. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  13. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  14. 2020FedDANE: A Federated Newton-Type Methodcited by 76
  15. 2022On the Importance and Applicability of Pre-Training for Federated Learningcited by 71
  16. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 69
  17. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  18. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  19. 2020FOCUS: Dealing with Label Quality Disparity in Federated Learningcited by 36
  20. 2021Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneitycited by 27
  21. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
  22. 2022Federated Learning with Position-Aware Neuronscited by 24
  23. 2023FedNoisy: Federated Noisy Label Learning Benchmarkcited by 11
  24. 2024Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Datacited by 11
  25. 2023A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Designcited by 5
  26. 2024Synthetic Data Aided Federated Learning Using Foundation Modelscited by 3

Built on by

Cited by year (2026 to 14 Oct)

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

4 papers.

  1. 2026FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learningcited by 2
  2. 2026FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labelscited by 1
  3. 2024Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labelscited by 0
  4. 2025Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Datacited by 0