mlsanityData to 14 Oct 2026

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

arXiv 2303.16270 ↗First posted 28 Mar 2023cs.LGcited by 13

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Abstract (arXiv)

Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data. Vertical federated learning (VFL) deals with scenarios in which the data on clients have different feature spaces but share some overlapping samples. Existing VFL approaches suffer from high communication costs and cannot deal efficiently with limited overlapping samples commonly seen in the real world. We propose a practical vertical federated learning (VFL) framework called one-shot VFL that can solve the communication bottleneck and the problem of limited overlapping samples simultaneously based on semi-supervised learning. We also propose few-shot VFL to improve the accuracy further with just one more communication round between the server and the clients. In our proposed framework, the clients only need to communicate with the server once or only a few times. We evaluate the proposed VFL framework on both image and tabular datasets. Our methods can improve the accuracy by more than 46.5% and reduce the communication cost by more than 330× compared with state-of-the-art VFL methods when evaluated on CIFAR-10. Our code will be made publicly available at urlhttps://nvidia.github.io/NVFlare/research/one-shot-vfl.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 28 Mar 2023.

Builds on

24 of 37 references.

References · 24

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  5. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 920
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  7. 2016Tutorial on Variational Autoencoderscited by 409
  8. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  9. 2015Semi-Supervised Learning with Ladder Networkscited by 247
  10. 2014Learning with Pseudo-Ensemblescited by 241
  11. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  12. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
12 more
  1. 2021Deep Learning with Label Differential Privacycited by 117
  2. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  3. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  4. 2019Federated Forestcited by 49
  5. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  6. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  7. 2020FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Trainingcited by 32
  8. 2022Label Leakage and Protection from Forward Embedding in Vertical Federated Learningcited by 27
  9. 2020Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japancited by 23
  10. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  11. 2020Semi-supervised Federated Learning for Activity Recognitioncited by 18
  12. 2019Learning Privately over Distributed Features: An ADMM Sharing Approachcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 5; 2026: 1.

13 papers.

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  1. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  2. 2024Vertical Federated Learning with Missing Features During Training and Inferencecited by 6
  3. 2024Empowering Federated Learning for Massive Models with NVIDIA FLAREcited by 5
  4. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  5. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  6. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  7. 2024Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Datacited by 1
  8. 2024Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlapcited by 0
  9. 2025Reliable Imputed-Sample Assisted Vertical Federated Learningcited by 0
  10. 2025FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learningcited by 0
  11. 2025Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Datacited by 0
  12. 2025BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneitycited by 0
1 more
  1. 2026Expected Gain-based Escalation in Vertical Federated Learningcited by 0