mlsanityData to 14 Oct 2026

FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity

arXiv 2406.18995 ↗First posted 27 Jun 2024cs.LG cs.AIcited by 5

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Abstract (arXiv)

Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy and has made significant progress in medical image classification. One common assumption is task homogeneity where each client has access to all classes during training. However, in clinical practice, given a multi-label classification task, constrained by the level of medical knowledge and the prevalence of diseases, each institution may diagnose only partial categories, resulting in task heterogeneity. How to pursue effective multi-label medical image classification under task heterogeneity is under-explored. In this paper, we first formulate such a realistic label missing setting in the multi-label FL domain and propose a two-stage method FedMLP to combat class missing from two aspects: pseudo label tagging and global knowledge learning. The former utilizes a warmed-up model to generate class prototypes and select samples with high confidence to supplement missing labels, while the latter uses a global model as a teacher for consistency regularization to prevent forgetting missing class knowledge. Experiments on two publicly-available medical datasets validate the superiority of FedMLP against the state-of-the-art both federated semi-supervised and noisy label learning approaches under task heterogeneity. Code is available at https://github.com/szbonaldo/FedMLP.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

18 of 27 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  4. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  5. 2020Long-tail learning via logit adjustmentcited by 618
  6. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  7. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  8. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  9. 2023Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learningcited by 40
  10. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  11. 2022RSCFed: Random Sampling Consensus Federated Semi-supervised Learningcited by 32
  12. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
6 more
  1. 2022FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classificationcited by 18
  2. 2023FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noisecited by 14
  3. 2022Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalancecited by 13
  4. 2023Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clientscited by 12
  5. 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
  6. 2023Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labelscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Neural Collapse-Inspired Multi-Label Federated Learning under Label-Distribution Skewcited by 2
  2. 2026Adaptive Disentangled Representation Learning for Incomplete Multi-View Multi-Label Classificationcited by 1
  3. 2026VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentationcited by 0
  4. 2026One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classificationcited by 0
  5. 2026Label Granularity Skew in Federated Learning with Hierarchical Image Classificationcited by 0