mlsanityData to 7 Oct 2026

RSCFed: Random Sampling Consensus Federated Semi-supervised Learning

arXiv 2203.13993 ↗First posted 26 Mar 2022cs.CVcited by 32

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Abstract (arXiv)

Federated semi-supervised learning (FSSL) aims to derive a global model by training fully-labeled and fully-unlabeled clients or training partially labeled clients. The existing approaches work well when local clients have independent and identically distributed (IID) data but fail to generalize to a more practical FSSL setting, i.e., Non-IID setting. In this paper, we present a Random Sampling Consensus Federated learning, namely RSCFed, by considering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients. Our key motivation is that given models with large deviations from either labeled clients or unlabeled clients, the consensus could be reached by performing random sub-sampling over clients. To achieve it, instead of directly aggregating local models, we first distill several sub-consensus models by random sub-sampling over clients and then aggregating the sub-consensus models to the global model. To enhance the robustness of sub-consensus models, we also develop a novel distance-reweighted model aggregation method. Experimental results show that our method outperforms state-of-the-art methods on three benchmarked datasets, including both natural and medical images. The code is available at https://github.com/XMed-Lab/RSCFed.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 28 of its 37 references that are in the map. First posted on arXiv 26 Mar 2022.

Builds on

28 of 37 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  8. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  9. 2021Model-Contrastive Federated Learningcited by 762
  10. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  11. 2020Federated Learning with Matched Averagingcited by 564
  12. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 462
16 more
  1. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  2. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  3. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  4. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  5. 2020PseudoSeg: Designing Pseudo Labels for Semantic Segmentationcited by 180
  6. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  7. 2019Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentationcited by 138
  8. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  9. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  10. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  11. 2021SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classificationcited by 68
  12. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  13. 2019Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Modelcited by 53
  14. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  15. 2021SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labelingcited by 29
  16. 2020Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Modelscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 11; 2024: 9; 2025: 4; 2026: 5.

32 papers.

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  1. 2023Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Modelcited by 33
  2. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  3. 2022Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentationcited by 27
  4. 2023Federated Learning without Full Labels: A Surveycited by 12
  5. 2023Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulatorscited by 9
  6. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  7. 2024A Mutual Information Perspective on Federated Contrastive Learningcited by 7
  8. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  9. 2023Towards Unbiased Training in Federated Open-world Semi-supervised Learningcited by 6
  10. 2024Robust Training of Federated Models with Extremely Label Deficiencycited by 6
  11. 2024Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learningcited by 5
  12. 2024FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneitycited by 5
20 more
  1. 2024(FL)2: Overcoming Few Labels in Federated Semi-Supervised Learningcited by 5
  2. 2023Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labelscited by 4
  3. 2023Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging datacited by 4
  4. 2024Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Datacited by 4
  5. 2022Dual Class-Aware Contrastive Federated Semi-Supervised Learningcited by 3
  6. 2023FedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysiscited by 3
  7. 2023FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encodercited by 3
  8. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  9. 2025CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environmentscited by 1
  10. 2026FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labelscited by 1
  11. 2022Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learningcited by 0
  12. 2023Federated Semi-Supervised Learning with Annotation Heterogeneitycited by 0
  13. 2024Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregationcited by 0
  14. 2025Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domainscited by 0
  15. 2025Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosiscited by 0
  16. 2025SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentationcited by 0
  17. 2026ProxyFL: A Proxy-Guided Framework for Federated Semi-Supervised Learningcited by 0
  18. 2026Federated Active Learning Under Extreme Non-IID and Global Class Imbalancecited by 0
  19. 2026Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Predictioncited by 0
  20. 2026One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classificationcited by 0