mlsanityData to 7 Oct 2026

FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data

arXiv 2103.03918 ↗First posted 5 Mar 2021cs.LG cs.AI cs.CRcited by 31

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Abstract (arXiv)

Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this paradigm, only model updates, such as model weights or gradients, are shared. Many existing approaches have focused on horizontal FL, where each party has the entire feature set and labels in the training data set. However, many real scenarios follow a vertically-partitioned FL setup, where a complete feature set is formed only when all the datasets from the parties are combined, and the labels are only available to a single party. Privacy-preserving vertical FL is challenging because complete sets of labels and features are not owned by one entity. Existing approaches for vertical FL require multiple peer-to-peer communications among parties, leading to lengthy training times, and are restricted to (approximated) linear models and just two parties. To close this gap, we propose FedV, a framework for secure gradient computation in vertical settings for several widely used ML models such as linear models, logistic regression, and support vector machines. FedV removes the need for peer-to-peer communication among parties by using functional encryption schemes; this allows FedV to achieve faster training times. It also works for larger and changing sets of parties. We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 25 of its 64 references that are in the map. First posted on arXiv 5 Mar 2021.

Builds on

25 of 64 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2019Towards Federated Learning at Scale: System Designcited by 826
  6. 2018How To Backdoor Federated Learningcited by 745
  7. 2017Federated Multi-Task Learningcited by 669
  8. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  9. 2018Detecting Backdoor Attacks on Deep Neural Networks by Activation Clusteringcited by 429
  10. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  11. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  12. 2018Machine Learning with Membership Privacy using Adversarial Regularizationcited by 177
13 more
  1. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  2. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  3. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  4. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 76
  5. 2018Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learningcited by 75
  6. 2019Variational Federated Multi-Task Learningcited by 69
  7. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  8. 2019HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learningcited by 51
  9. 2019A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regressioncited by 37
  10. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  11. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 35
  12. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
  13. 2020Hybrid Differentially Private Federated Learning on Vertically Partitioned Datacited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 7; 2024: 8; 2025: 8; 2026: 2.

31 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2022A Survey on Heterogeneous Federated Learningcited by 42
  3. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  4. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  5. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  6. 2022Continual Horizontal Federated Learning for Heterogeneous Datacited by 8
  7. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  8. 2024Responsible Artificial Intelligence: A Structured Literature Reviewcited by 6
  9. 2024Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioningcited by 6
  10. 2025TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learningcited by 4
  11. 2023GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Datacited by 3
  12. 2024Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detectioncited by 3
19 more
  1. 2024A Differentially Private Blockchain-Based Approach for Vertical Federated Learningcited by 3
  2. 2025The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communicationscited by 3
  3. 2023Quadratic Functional Encryption for Secure Training in Vertical Federated Learningcited by 1
  4. 2023Federated Learning over Harmonized Data Siloscited by 1
  5. 2023Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddingscited by 1
  6. 2023Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detectioncited by 1
  7. 2024A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoTcited by 1
  8. 2025OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learningcited by 1
  9. 2022Efficient and Privacy Preserving Group Signature for Federated Learningcited by 0
  10. 2023EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learningcited by 0
  11. 2023FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix Factorizationcited by 0
  12. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  13. 2025PBM-VFL: Vertical Federated Learning with Feature and Sample Privacycited by 0
  14. 2025Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimizationcited by 0
  15. 2025FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learningcited by 0
  16. 2025On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attackcited by 0
  17. 2025Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noisecited by 0
  18. 2026Secure and Privacy-Preserving Vertical Federated Learningcited by 0
  19. 2026Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitablecited by 0