mlsanityData to 7 Oct 2026

Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating

arXiv 2103.00958 ↗First posted 1 Mar 2021cs.LGcited by 37

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Abstract (arXiv)

Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. In the real VFL applications, usually only one or partial parties hold labels, which makes it challenging for all parties to collaboratively learn the model without privacy leakage. Meanwhile, most existing VFL algorithms are trapped in the synchronous computations, which leads to inefficiency in their real-world applications. To address these challenging problems, we propose a novel bf VFL framework integrated with new bf backward updating mechanism and bf bilevel asynchronous parallel architecture (VFB2), under which three new algorithms, including VFB2-SGD, -SVRG, and -SAGA, are proposed. We derive the theoretical results of the convergence rates of these three algorithms under both strongly convex and nonconvex conditions. We also prove the security of VFB2 under semi-honest threat models. Extensive experiments on benchmark datasets demonstrate that our algorithms are efficient, scalable and lossless.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

17 of 36 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2017Federated Multi-Task Learningcited by 669
  4. 2014SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectivescited by 401
  5. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  6. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  7. 2015Asynchronous Parallel Stochastic Gradient for Nonconvex Optimizationcited by 115
  8. 2019HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learningcited by 51
  9. 2019Federated Forestcited by 49
  10. 2016Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimizationcited by 37
  11. 2019A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regressioncited by 37
  12. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 35
5 more
  1. 2020Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learningcited by 19
  2. 2016ASAGA: Asynchronous Parallel SAGAcited by 18
  3. 2020Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimizationcited by 10
  4. 2018Feature-Distributed SVRG for High-Dimensional Linear Classificationcited by 8
  5. 2020Faster Stochastic Quasi-Newton Methodscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 15; 2023: 10; 2024: 4; 2025: 2; 2026: 1.

37 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  3. 2022A Survey on Heterogeneous Federated Learningcited by 42
  4. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  5. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  6. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  7. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  8. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  9. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  10. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  11. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  12. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
25 more
  1. 2021H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learningcited by 14
  2. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  3. 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
  4. 2022Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMcited by 10
  5. 2022Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithmcited by 9
  6. 2021Achieving Model Fairness in Vertical Federated Learningcited by 8
  7. 2022Continual Horizontal Federated Learning for Heterogeneous Datacited by 8
  8. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  9. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  10. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  11. 2022Secure Forward Aggregation for Vertical Federated Neural Networkscited by 6
  12. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  13. 2022FedGBF: An efficient vertical federated learning framework via gradient boosting and baggingcited by 5
  14. 2021A Vertical Federated Learning Framework for Horizontally Partitioned Labelscited by 4
  15. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  16. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  17. 2023Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimizationcited by 2
  18. 2023Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environmentscited by 2
  19. 2022Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restrictioncited by 0
  20. 2023EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learningcited by 0
  21. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  22. 2024TablePuppet: A Generic Framework for Relational Federated Learningcited by 0
  23. 2025Event-Driven Online Vertical Federated Learningcited by 0
  24. 2025PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecturecited by 0
  25. 2026Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systemscited by 0