mlsanityData to 7 Oct 2026

SplitNN-driven Vertical Partitioning

arXiv 2008.04137 ↗First posted 7 Aug 2020cs.LG stat.MLcited by 42

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Abstract (arXiv)

In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distributed features. SplitNN does not share raw data or model details with collaborating institutions. The proposed configuration allows training among institutions holding diverse sources of data without the need of complex encryption algorithms or secure computation protocols. We evaluate several configurations to merge the outputs of the split models, and compare performance and resource efficiency. The method is flexible and allows many different configurations to tackle the specific challenges posed by vertically split datasets.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 10; 2023: 10; 2024: 10; 2025: 4; 2026: 2.

42 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  3. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  4. 2021Practical Defences Against Model Inversion Attacks for Split Neural Networkscited by 29
  5. 2022Differentially Private Label Protection in Split Learningcited by 25
  6. 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
  7. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  8. 2022Similarity-based Label Inference Attack against Training and Inference of Split Learningcited by 15
  9. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  10. 2023LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learningcited by 14
  11. 2022Vertical Semi-Federated Learning for Efficient Online Advertisingcited by 12
  12. 2021Self-supervised Cross-silo Federated Neural Architecture Searchcited by 11
30 more
  1. 2022Vertical Federated Edge Learning with Distributed Integrated Sensing and Communicationcited by 11
  2. 2023A Split-and-Privatize Framework for Large Language Model Fine-Tuningcited by 11
  3. 2023Passive Inference Attacks on Split Learning via Adversarial Regularizationcited by 9
  4. 2021In-Network Learning: Distributed Training and Inference in Networkscited by 8
  5. 2023Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attackscited by 8
  6. 2022DPAUC: Differentially Private AUC Computation in Federated Learningcited by 7
  7. 2024Vertical Federated Learning with Missing Features During Training and Inferencecited by 6
  8. 2021Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Ownerscited by 5
  9. 2022VFed-SSD: Towards Practical Vertical Federated Advertisingcited by 5
  10. 2024Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detectioncited by 3
  11. 2024Communication-efficient Vertical Federated Learning via Compressed Error Feedbackcited by 3
  12. 2022Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenariocited by 2
  13. 2023Multi-limb Split Learning for Tumor Classification on Vertically Distributed Datacited by 2
  14. 2023A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learningcited by 2
  15. 2023Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environmentscited by 2
  16. 2024Scalable Vertical Federated Learning via Data Augmentation and Amortized Inferencecited by 2
  17. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  18. 2026Floe: Federated Specialization for Real-Time LLM-SLM Inferencecited by 2
  19. 2023Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddingscited by 1
  20. 2024Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Datacited by 1
  21. 2024Empowering Data Mesh with Federated Learningcited by 1
  22. 2024TabVFL: Improving Latent Representation in Vertical Federated Learningcited by 1
  23. 2025Scaling Probabilistic Circuits via Data Partitioningcited by 1
  24. 2025REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Featurecited by 1
  25. 2023GAN-based Vertical Federated Learning for Label Protection in Binary Classificationcited by 0
  26. 2024Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industrycited by 0
  27. 2024De-VertiFL: A Solution for Decentralized Vertical Federated Learningcited by 0
  28. 2025Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networkscited by 0
  29. 2025Enhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approachescited by 0
  30. 2026Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learningcited by 0