mlsanityData to 7 Oct 2026

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv 2204.10836 ↗First posted 22 Apr 2022cs.LG eess.IVcited by 83

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Abstract (arXiv)

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). Builds on 39 of its 158 references that are in the map. First posted on arXiv 22 Apr 2022.

Builds on

39 of 158 references.

References · 39

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 21; 2024: 26; 2025: 19; 2026: 14.

83 papers.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
  3. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  4. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  5. 2023Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognitioncited by 19
  6. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  7. 2023Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imagingcited by 17
  8. 2023FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolationcited by 14
  9. 2023FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Schedulercited by 13
  10. 2023Federated Learning of Medical Concepts Embedding using BEHRTcited by 11
  11. 2024BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023cited by 11
  12. 2023Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applicationscited by 10
38 more
  1. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  2. 2024Tackling Data Heterogeneity in Federated Learning via Loss Decompositioncited by 9
  3. 2024Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imagingcited by 8
  4. 2024Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcarecited by 7
  5. 2024Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clientscited by 7
  6. 2023Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacycited by 6
  7. 2025From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Researchcited by 6
  8. 2022Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site datacited by 5
  9. 2024Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gaincited by 5
  10. 2024Worldwide Federated Training of Language Modelscited by 5
  11. 2024Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalitiescited by 5
  12. 2024Federated brain tumor segmentation: an extensive benchmarkcited by 5
  13. 2023Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentationcited by 4
  14. 2023Privacy-preserving patient clustering for personalized federated learningcited by 4
  15. 2025Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detectioncited by 4
  16. 2023FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classificationcited by 3
  17. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  18. 2024Communication-Efficient Federated Group Distributionally Robust Optimizationcited by 3
  19. 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentationcited by 3
  20. 2023Federated attention consistent learning models for prostate cancer diagnosis and Gleason gradingcited by 2
  21. 2023Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Modelscited by 2
  22. 2023Brain MRI Screening Tool with Federated Learningcited by 2
  23. 2023FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settingscited by 2
  24. 2024Multi-modal MRI Translation via Evidential Regression and Distribution Calibrationcited by 2
  25. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  26. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  27. 2025Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentationcited by 2
  28. 2025FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentationcited by 2
  29. 2025Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunitiescited by 2
  30. 2025The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learningcited by 2
  31. 2022MammoFL: Mammographic Breast Density Estimation using Federated Learningcited by 1
  32. 2023Medical Federated Model with Mixture of Personalized and Sharing Componentscited by 1
  33. 2024On the Conflict of Robustness and Learning in Collaborative Machine Learningcited by 1
  34. 2025PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learningcited by 1
  35. 2025Federated learning, ethics, and the double black box problem in medical AIcited by 1
  36. 2025Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapycited by 1
  37. 2025Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AIcited by 1
  38. 2025Multi-modal brain MRI synthesis based on SwinUNETRcited by 1

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