mlsanityData to 14 Oct 2026

A Survey on Contribution Evaluation in Vertical Federated Learning

arXiv 2405.02364 ↗First posted 3 May 2024cs.LG cs.DCcited by 5

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Abstract (arXiv)

Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing. VFL facilitates collaboration among multiple entities with distinct feature sets on the same user population, enabling the joint training of predictive models without direct data sharing. A key aspect of VFL is the fair and accurate evaluation of each entity's contribution to the learning process. This is crucial for maintaining trust among participating entities, ensuring equitable resource sharing, and fostering a sustainable collaboration framework. This paper provides a thorough review of contribution evaluation in VFL. We categorize the vast array of contribution evaluation techniques along the VFL lifecycle, granularity of evaluation, privacy considerations, and core computational methods. We also explore various tasks in VFL that involving contribution evaluation and analyze their required evaluation properties and relation to the VFL lifecycle phases. Finally, we present a vision for the future challenges of contribution evaluation in VFL. By providing a structured analysis of the current landscape and potential advancements, this paper aims to guide researchers and practitioners in the design and implementation of more effective, efficient, and privacy-centric VFL solutions. Relevant literature and open-source resources have been compiled and are being continuously updated at the GitHub repository: urlhttps://github.com/cuiyuebing/VFLCE.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 52 of its 92 references that are in the map. First posted on arXiv 3 May 2024.

Builds on

52 of 92 references.

References · 52

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38 more
  1. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 106
  2. 2022The Shapley Value in Machine Learningcited by 98
  3. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
  4. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  5. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  6. 2019Measure Contribution of Participants in Federated Learningcited by 79
  7. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 74
  8. 2020Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applicationscited by 58
  9. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
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  11. 2020A Survey on Data Pricing: from Economics to Data Sciencecited by 39
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  14. 2019Interpret Federated Learning with Shapley Valuescited by 33
  15. 2022Trading Off Privacy, Utility and Efficiency in Federated Learningcited by 33
  16. 2020Federated Transfer Learning: concept and applicationscited by 32
  17. 2021Defending against Reconstruction Attack in Vertical Federated Learningcited by 20
  18. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  19. 2021Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Datacited by 18
  20. 2021Data Pricing in Machine Learning Pipelinescited by 18
  21. 2022Fair and efficient contribution valuation for vertical federated learningcited by 18
  22. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  23. 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning Systemcited by 16
  24. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  25. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  26. 2023FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learningcited by 15
  27. 2023LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learningcited by 14
  28. 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
  29. 2022OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitizationcited by 12
  30. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
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  34. 2023FedZKP: Federated Model Ownership Verification with Zero-knowledge Proofcited by 9
  35. 2023Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaborationcited by 6
  36. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  37. 2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
  38. 2023Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problemcited by 2

50 most cited of 52 All 52 (JSON)

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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  2. 2025Efficient Leave-one-out Approximation in LLM Multi-agent Debate Based on Introspectioncited by 4
  3. 2025OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learningcited by 1
  4. 2025On the Fragility of Contribution Score Computation in Federated Learningcited by 1
  5. 2025Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutationcited by 0