mlsanityData to 7 Oct 2026

FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection

arXiv 2408.09227 ↗First posted 17 Aug 2024cs.AIcited by 8

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Abstract (arXiv)

This study introduces the Federated Medical Knowledge Injection (FEDMEKI) platform, a new benchmark designed to address the unique challenges of integrating medical knowledge into foundation models under privacy constraints. By leveraging a cross-silo federated learning approach, FEDMEKI circumvents the issues associated with centralized data collection, which is often prohibited under health regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the USA. The platform is meticulously designed to handle multi-site, multi-modal, and multi-task medical data, which includes 7 medical modalities, including images, signals, texts, laboratory test results, vital signs, input variables, and output variables. The curated dataset to validate FEDMEKI covers 8 medical tasks, including 6 classification tasks (lung opacity detection, COVID-19 detection, electrocardiogram (ECG) abnormal detection, mortality prediction, sepsis prediction, and enlarged cardiomediastinum detection) and 2 generation tasks (medical visual question answering (MedVQA) and ECG noise clarification). This comprehensive dataset is partitioned across several clients to facilitate the decentralized training process under 16 benchmark approaches. FEDMEKI not only preserves data privacy but also enhances the capability of medical foundation models by allowing them to learn from a broader spectrum of medical knowledge without direct data exposure, thereby setting a new benchmark in the application of foundation models within the healthcare sector.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 46 of its 85 references that are in the map. First posted on arXiv 17 Aug 2024.

Builds on

46 of 85 references.

References · 46

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2023Visual Instruction Tuningcited by 7,792
  6. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  7. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  8. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  9. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  10. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,324
  11. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,287
  12. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,271
34 more
  1. 2020Adaptive Federated Optimizationcited by 1,036
  2. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 969
  3. 2020The Future of Digital Health with Federated Learningcited by 625
  4. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  5. 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 525
  6. 2020PathVQA: 30000+ Questions for Medical Visual Question Answeringcited by 467
  7. 2021SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answeringcited by 444
  8. 2023A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPTcited by 364
  9. 2023PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answeringcited by 333
  10. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  11. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
  12. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  13. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  14. 2024Towards Building Multilingual Language Model for Medicinecited by 91
  15. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
  16. 2023FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learningcited by 70
  17. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
  18. 2023FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Modelscited by 62
  19. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
  20. 2022Communication-Efficient Adaptive Federated Learningcited by 55
  21. 2023ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogramcited by 52
  22. 2023Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AIcited by 32
  23. 2023Foundation Models Meet Visualizations: Challenges and Opportunitiescited by 31
  24. 2023Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical MLcited by 28
  25. 2022Mini-Model Adaptation: Efficiently Extending Pretrained Models to New Languages via Aligned Shallow Trainingcited by 24
  26. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 22
  27. 2021Multi-task Federated Learning for Heterogeneous Pancreas Segmentationcited by 19
  28. 2024FedPFT: Federated Proxy Fine-Tuning of Foundation Modelscited by 16
  29. 2023Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfercited by 13
  30. 2021Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimizationcited by 12
  31. 2023Hierarchical Pretraining on Multimodal Electronic Health Recordscited by 11
  32. 2024Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learningcited by 11
  33. 2024Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagationcited by 5
  34. 2023CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interactioncited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025Vision Foundation Models in Medical Image Analysis: Advances and Challengescited by 9
  2. 2025Foundation Models in Medical Imaging: A Review and Outlookcited by 7
  3. 2025FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensingcited by 1
  4. 2025Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insightscited by 1
  5. 2025On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility Viewcited by 0
  6. 2026Knowledge Transfer Scaling Laws for 3D Medical Imagingcited by 0
  7. 2026Can Broad Biomedical Knowledge be Contextualized into Scenario-Grounded Propositions?cited by 0
  8. 2026Applying Language Models in Clinical Medicine: Recent Trends and Perspectivescited by 0