mlsanityData to 7 Oct 2026

Split Learning for collaborative deep learning in healthcare

arXiv 1912.12115 ↗First posted 27 Dec 2019cs.LG cs.DC stat.MLcited by 72

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Abstract (arXiv)

Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-center collaborative studies are a burden to set up. Distributed machine learning methods promise to mitigate these problems. We argue for a split learning based approach and apply this distributed learning method for the first time in the medical field to compare performance against (1) centrally hosted and (2) non collaborative configurations for a range of participants. Two medical deep learning tasks are used to compare split learning to conventional single and multi center approaches: a binary classification problem of a data set of 9000 fundus photos, and multi-label classification problem of a data set of 156,535 chest X-rays. The several distributed learning setups are compared for a range of 1-50 distributed participants. Performance of the split learning configuration remained constant for any number of clients compared to a single center study, showing a marked difference compared to the non collaborative configuration after 2 clients (p < 0.001) for both sets. Our results affirm the benefits of collaborative training of deep neural networks in health care. Our work proves the significant benefit of distributed learning in healthcare, and paves the way for future real-world implementations.

Key facts. Cited by 72 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Dec 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 9; 2023: 18; 2024: 17; 2025: 11; 2026: 6.

72 papers.

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  1. 2021FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learningcited by 39
  2. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  3. 2022Knowledge Distillation for Federated Learning: a Practical Guidecited by 31
  4. 2021Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthinesscited by 15
  5. 2022Similarity-based Label Inference Attack against Training and Inference of Split Learningcited by 15
  6. 2022Federated Learning of Large Models at the Edge via Principal Sub-Model Trainingcited by 13
  7. 2023ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic Tensor Selectioncited by 12
  8. 2021Self-supervised Cross-silo Federated Neural Architecture Searchcited by 11
  9. 2022Robust Split Federated Learning for U-shaped Medical Image Networkscited by 11
  10. 2023Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Informationcited by 11
  11. 2021Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performancecited by 10
  12. 2023Passive Inference Attacks on Split Learning via Adversarial Regularizationcited by 9
38 more
  1. 2021Personalized Federated Deep Learning for Pain Estimation From Face Imagescited by 8
  2. 2021AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learningcited by 8
  3. 2021Federated Learning-based Active Authentication on Mobile Devicescited by 7
  4. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  5. 2023FeSViBS: Federated Split Learning of Vision Transformer with Block Samplingcited by 7
  6. 2024A Mutual Information Perspective on Federated Contrastive Learningcited by 7
  7. 2025SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)cited by 7
  8. 2025Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approachcited by 7
  9. 2023Split Without a Leak: Reducing Privacy Leakage in Split Learningcited by 6
  10. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  11. 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platformscited by 5
  12. 2022FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learningcited by 4
  13. 2022Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentationcited by 4
  14. 2023SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detectioncited by 4
  15. 2023Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotationscited by 4
  16. 2022Measuring and Controlling Split Layer Privacy Leakage Using Fisher Informationcited by 3
  17. 2022FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Trainingcited by 3
  18. 2023PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientscited by 3
  19. 2023SLPerf: a Unified Framework for Benchmarking Split Learningcited by 3
  20. 2024URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learningcited by 3
  21. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  22. 2024Parallel Split Learning with Global Samplingcited by 3
  23. 2025Federated Granger Causality Learning for Interdependent Clients with State Space Representationcited by 3
  24. 2025Federated Split Learning with Improved Communication and Storage Efficiencycited by 3
  25. 2026HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Trainingcited by 3
  26. 2021Vulnerability Due to Training Order in Split Learningcited by 2
  27. 2023Multi-limb Split Learning for Tumor Classification on Vertically Distributed Datacited by 2
  28. 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inferencecited by 2
  29. 2024Scalable Vertical Federated Learning via Data Augmentation and Amortized Inferencecited by 2
  30. 2024Inference Attacks: A Taxonomy, Survey, and Promising Directionscited by 2
  31. 2024SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problemscited by 2
  32. 2024Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformationcited by 2
  33. 2024Split Learning in Computer Vision for Semantic Segmentation Delay Minimizationcited by 2
  34. 2021Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networkscited by 1
  35. 2023Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visionscited by 1
  36. 2023Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theorycited by 1
  37. 2024Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemblecited by 1
  38. 2024TabVFL: Improving Latent Representation in Vertical Federated Learningcited by 1

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