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PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN

arXiv 2104.00489 ↗First posted 1 Apr 2021cs.LG cs.CR cs.DCcited by 35

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Abstract (arXiv)

We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on data features vertically partitioned across multiple owners while keeping raw data on an owner's device. To link entities shared across different datasets' partitions, we use Private Set Intersection on IDs associated with data points. To demonstrate the validity of the proposed framework, we present the training of a simple dual-headed split neural network for a MNIST classification task, with data samples vertically distributed across two data owners and a data scientist.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 16 of its 42 references that are in the map. First posted on arXiv 1 Apr 2021.

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16 of 42 references.

References · 16

2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
2019Towards Federated Learning at Scale: System Designcited by 826
2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
2020SplitFed: When Federated Learning Meets Split Learningcited by 308
2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
2018Distributed learning of deep neural network over multiple agentscited by 277
2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
2018A generic framework for privacy preserving deep learningcited by 103
2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
2020SplitNN-driven Vertical Partitioningcited by 42
2020Asymmetrical Vertical Federated Learningcited by 26

4 more

2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
2021Privacy and Trust Redefined in Federated Machine Learningcited by 13
2020Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learningcited by 12
2020A Distributed Trust Framework for Privacy-Preserving Machine Learningcited by 7

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Cited by year

Citing papers per year: 2021: 3; 2022: 12; 2023: 7; 2024: 9; 2025: 4.

35 papers.

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2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
2022A Survey on Heterogeneous Federated Learningcited by 42
2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
2021Practical Defences Against Model Inversion Attacks for Split Neural Networkscited by 29
2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
2022Similarity-based Label Inference Attack against Training and Inference of Split Learningcited by 15
2022FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clientscited by 14
2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14

23 more

2022Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learningcited by 13
2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
2023A Split-and-Privatize Framework for Large Language Model Fine-Tuningcited by 11
2024Training Machine Learning models at the Edge: A Surveycited by 11
2022Continual Horizontal Federated Learning for Heterogeneous Datacited by 8
2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
2023Personalised Federated Learning On Heterogeneous Feature Spacescited by 5
2023GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Datacited by 3
2024Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detectioncited by 3
2025Cooperative Decentralized Backdoor Attacks on Vertical Federated Learningcited by 3
2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
2025The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communicationscited by 3
2023Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detectioncited by 1
2024Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systemscited by 1
2024Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Datacited by 1
2024TabVFL: Improving Latent Representation in Vertical Federated Learningcited by 1
2025PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaborationcited by 1
2021An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraintscited by 0
2022Vertical Federated Linear Contextual Banditscited by 0
2023EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learningcited by 0
2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
2024De-VertiFL: A Solution for Decentralized Vertical Federated Learningcited by 0