mlsanityData to 7 Oct 2026

Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey

arXiv 2405.17495 ↗First posted 25 May 2024cs.LG cs.CRcited by 14

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Abstract (arXiv)

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared samples, without leaking private data. Recent research has shown promising results addressing various challenges in VFL, highlighting its potential for practical applications in cross-domain collaboration. However, the corresponding research is scattered and lacks organization. To advance VFL research, this survey offers a systematic overview of recent developments. First, we provide a history and background introduction, along with a summary of the general training protocol of VFL. We then revisit the taxonomy in recent reviews and analyze limitations in-depth. For a comprehensive and structured discussion, we synthesize recent research from three fundamental perspectives: effectiveness, security, and applicability. Finally, we discuss several critical future research directions in VFL, which will facilitate the developments in this field. We provide a collection of research lists and periodically update them at https://github.com/shentt67/VFL_Survey.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 109 of its 215 references that are in the map. First posted on arXiv 25 May 2024.

Builds on

109 of 215 references.

References · 109

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38 more
  1. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  2. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
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  6. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
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  14. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
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  22. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 6.

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  1. 2025Backdoor Cleaning without External Guidance in MLLM Fine-tuningcited by 34
  2. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  3. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  4. 2025A Survey on Foundation Models for Personalized Federated Intelligencecited by 2
  5. 2026FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphscited by 2
  6. 2025Oops!... They Stole it Again: Attacks on Split Learningcited by 1
  7. 2025Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimizationcited by 0
  8. 2025VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Maskingcited by 0
  9. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  10. 2026VertCoHiRF: Decentralized Vertical Clustering Beyond k-meanscited by 0
  11. 2026Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defendcited by 0
  12. 2026CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learningcited by 0
2 more
  1. 2026From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learningcited by 0
  2. 2026Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practicecited by 0