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DVFL: A Vertical Federated Learning Method for Dynamic Data

arXiv 2111.03341 ↗First posted 5 Nov 2021cs.LG cs.AIcited by 1

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Abstract (arXiv)

Federated learning, which solves the problem of data island by connecting multiple computational devices into a decentralized system, has become a promising paradigm for privacy-preserving machine learning. This paper studies vertical federated learning (VFL), which tackles the scenarios where collaborating organizations share the same set of users but disjoint features. Contemporary VFL methods are mainly used in static scenarios where the active party and the passive party have all the data from the beginning and will not change. However, the data in real life often changes dynamically. To alleviate this problem, we propose a new vertical federation learning method, DVFL, which adapts to dynamic data distribution changes through knowledge distillation. In DVFL, most of the computations are held locally to improve data security and model efficiency. Our extensive experimental results show that DVFL can not only obtain results close to existing VFL methods in static scenes, but also adapt to changes in data distribution in dynamic scenarios.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 5 Nov 2021.

Builds on

9 of 21 references.

References · 9

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  3. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  4. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  5. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  6. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  7. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  8. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 36
  9. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23

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Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17

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  1. 2025Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learning
  2. 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
  3. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
  4. 2024FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning
  5. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation
  6. 2025Unlearning Clients, Features and Samples in Vertical Federated Learning
  7. 2026Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice
  8. 2025Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning