mlsanityData to 7 Oct 2026

FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification

arXiv 2206.13803 ↗First posted 28 Jun 2022cs.CV cs.AIcited by 18

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Abstract (arXiv)

Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering the ubiquitous class imbalance in medical data, FL can exhibit performance degradation, especially for minority classes (e.g. rare diseases). Existing methods towards this problem mainly focus on training a balanced classifier to eliminate class prior bias among classes, but neglect to explore better representation to facilitate classification performance. In this paper, we present a privacy-preserving FL method named FedIIC to combat class imbalance from two perspectives: feature learning and classifier learning. In feature learning, two levels of contrastive learning are designed to extract better class-specific features with imbalanced data in FL. In classifier learning, per-class margins are dynamically set according to real-time difficulty and class priors, which helps the model learn classes equally. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both real-world and simulated multi-source medical imaging data under class imbalance. Code is available at https://github.com/wnn2000/FedIIC.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 28 Jun 2022.

Builds on

25 of 45 references.

References · 25

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2020Supervised Contrastive Learningcited by 2,803
  6. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
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  8. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 987
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  10. 2021Model-Contrastive Federated Learningcited by 762
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13 more
  1. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  2. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  3. 2019BCN20000: Dermoscopic Lesions in the Wildcited by 224
  4. 2021Targeted Supervised Contrastive Learning for Long-Tailed Recognitioncited by 132
  5. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 80
  6. 2022Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Featurescited by 63
  7. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 62
  8. 2020Federated learning with class imbalance reductioncited by 52
  9. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  10. 2021Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognitioncited by 33
  11. 2020Fed-Focal Loss for imbalanced data classification in Federated Learningcited by 26
  12. 2022Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalancecited by 13
  13. 2022Flexible Sampling for Long-tailed Skin Lesion Classificationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 6; 2026: 4.

18 papers.

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  1. 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 28
  2. 2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 26
  3. 2023Federated Domain Generalization: A Surveycited by 25
  4. 2025Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learningcited by 11
  5. 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
  6. 2024FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneitycited by 5
  7. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  8. 2024FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completenesscited by 2
  9. 2024Overcoming label shift with target-aware federated learningcited by 2
  10. 2025Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEscited by 2
  11. 2025Neural Collapse-Inspired Multi-Label Federated Learning under Label-Distribution Skewcited by 2
  12. 2025Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentationcited by 1
6 more
  1. 2025Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matchingcited by 0
  2. 2025MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentationcited by 0
  3. 2026FedHypeVAE: Federated Learning with Hypernetwork Generated Conditional VAEs for Differentially Private Embedding Sharingcited by 0
  4. 2026Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clientscited by 0
  5. 2026Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generationcited by 0
  6. 2026Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learningcited by 0