Data to 7 Oct 2026

Split learning for health: Distributed deep learning without sharing raw patient data

arXiv 1812.00564 ↗First posted 3 Dec 2018cs.LG stat.MLcited by 391

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Abstract (arXiv)

Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding different modalities of patient data, ii) centralized and local health entities collaborating on multiple tasks and iii) learning without sharing labels. We compare performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.

Key facts. Cited by 391 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 55; 2022: 62; 2023: 76; 2024: 67; 2025: 69; 2026: 62.

391 papers.

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2021Towards Personalized Federated Learningcited by 462
2021A Field Guide to Federated Optimizationcited by 312
2022Transformers in Medical Imaging: A Surveycited by 245
2021Federated Learning for Smart Healthcare: A Surveycited by 182
2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 89
2021Label Leakage and Protection in Two-party Split Learningcited by 87
2023Split Learning in 6G Edge Networkscited by 68

38 more

2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 60
2021UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learningcited by 52
2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
2022A Survey on Heterogeneous Federated Learningcited by 42
2021FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learningcited by 39
2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
2021FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Modelscited by 31
2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
2023Split-and-Denoise: Protect large language model inference with local differential privacycited by 30
2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 30
2021Practical Defences Against Model Inversion Attacks for Split Neural Networkscited by 29
2021IPLS : A Framework for Decentralized Federated Learningcited by 26
2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
2022Label Leakage and Protection from Forward Embedding in Vertical Federated Learningcited by 26
2022Differentially Private Label Protection in Split Learningcited by 25
2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
2023GradientCoin: A Peer-to-Peer Decentralized Large Language Modelscited by 23
2021Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learningcited by 22
2023Reducing Communication for Split Learning by Randomized Top-k Sparsificationcited by 22
2023Federated Learning for Computer Visioncited by 22
2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
2021Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Thingscited by 20
2021SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learningcited by 20
2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
2022SplitGP: Achieving Both Generalization and Personalization in Federated Learningcited by 20
2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
2022A Survey on Over-the-Air Computationcited by 19
2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
2024WDMoE: Wireless Distributed Large Language Models with Mixture of Expertscited by 18
2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17

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