mlsanityData to 14 Oct 2026

LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning

arXiv 2305.02219 ↗First posted 3 May 2023cs.LG cs.DCcited by 14

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Abstract (arXiv)

We propose LESS-VFL, a communication-efficient feature selection method for distributed systems with vertically partitioned data. We consider a system of a server and several parties with local datasets that share a sample ID space but have different feature sets. The parties wish to collaboratively train a model for a prediction task. As part of the training, the parties wish to remove unimportant features in the system to improve generalization, efficiency, and explainability. In LESS-VFL, after a short pre-training period, the server optimizes its part of the global model to determine the relevant outputs from party models. This information is shared with the parties to then allow local feature selection without communication. We analytically prove that LESS-VFL removes spurious features from model training. We provide extensive empirical evidence that LESS-VFL can achieve high accuracy and remove spurious features at a fraction of the communication cost of other feature selection approaches.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 10 of its 34 references that are in the map. First posted on arXiv 3 May 2023.

Builds on

10 of 34 references.

References · 10

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2017Multitask learning and benchmarking with clinical time series datacited by 290
  3. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  4. 2020SplitNN-driven Vertical Partitioningcited by 42
  5. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  6. 2020Consistent Feature Selection for Analytic Deep Neural Networkscited by 27
  7. 2023FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learningcited by 15
  8. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  9. 2021Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learningcited by 10
  10. 2022HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learningcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 7; 2026: 3.

14 papers.

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  1. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  2. 2023ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learningcited by 9
  3. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  4. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  5. 2025RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budgetcited by 2
  6. 2025Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimizationcited by 1
  7. 2025Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffscited by 1
  8. 2025ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learningcited by 0
  9. 2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0
  10. 2025Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigmscited by 0
  11. 2025Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Datacited by 0
  12. 2026Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systemscited by 0
2 more
  1. 2026Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networkscited by 0
  2. 2026Expected Gain-based Escalation in Vertical Federated Learningcited by 0