mlsanityData to 14 Oct 2026

Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator

arXiv 1911.09824 ↗First posted 22 Nov 2019cs.LG cs.CR cs.DCcited by 52

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Abstract (arXiv)

Federated Learning is a new distributed learning mechanism which allows model training on a large corpus of decentralized data owned by different data providers, without sharing or leakage of raw data. According to the characteristics of data dis-tribution, it could be usually classified into three categories: horizontal federated learning, vertical federated learning, and federated transfer learning. In this paper we present a solution for parallel dis-tributed logistic regression for vertical federated learning. As compared with existing works, the role of third-party coordinator is removed in our proposed solution. The system is built on the pa-rameter server architecture and aims to speed up the model training via utilizing a cluster of servers in case of large volume of training data. We also evaluate the performance of the parallel distributed model training and the experimental results show the great scalability of the system.

Key facts. Cited by 52 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Nov 2019.

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Citing papers per year: 2021: 13; 2022: 15; 2023: 12; 2024: 8; 2025: 3; 2026: 1.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  2. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  3. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  4. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  5. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  6. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  7. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  8. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  9. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  10. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  11. 2023Federated Learning for Computer Visioncited by 22
  12. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
38 more
  1. 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
  2. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  3. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  4. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  5. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  6. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  7. 2021Self-supervised Cross-silo Federated Neural Architecture Searchcited by 11
  8. 2022Residue-based Label Protection Mechanisms in Vertical Logistic Regressioncited by 10
  9. 2022Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMcited by 10
  10. 2022A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learningcited by 10
  11. 2021PIVODL: Privacy-preserving vertical federated learning over distributed labelscited by 9
  12. 2021Enabling SQL-based Training Data Debugging for Federated Learningcited by 8
  13. 2022Continual Horizontal Federated Learning for Heterogeneous Datacited by 8
  14. 2023Federated Learning of Models Pre-Trained on Different Features with Consensus Graphscited by 8
  15. 2021Multi-Tier Federated Learning for Vertically Partitioned Datacited by 7
  16. 2022A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challengescited by 7
  17. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  18. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  19. 2021Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioningcited by 6
  20. 2022Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learningcited by 6
  21. 2022EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Partycited by 5
  22. 2021A Vertical Federated Learning Framework for Horizontally Partitioned Labelscited by 4
  23. 2021Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCAcited by 3
  24. 2023GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Datacited by 3
  25. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  26. 2025Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learningcited by 3
  27. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  28. 2023XFL: A High Performace, Lightweighted Federated Learning Frameworkcited by 2
  29. 2024AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learningcited by 2
  30. 2023FedST: Secure Federated Shapelet Transformation for Time Series Classificationcited by 1
  31. 2023Quadratic Functional Encryption for Secure Training in Vertical Federated Learningcited by 1
  32. 2023Federated Learning over Harmonized Data Siloscited by 1
  33. 2024TabVFL: Improving Latent Representation in Vertical Federated Learningcited by 1
  34. 2022Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restrictioncited by 0
  35. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  36. 2024Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systemscited by 0
  37. 2024De-VertiFL: A Solution for Decentralized Vertical Federated Learningcited by 0
  38. 2024A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Examplecited by 0

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