mlsanityData to 7 Oct 2026

Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data

arXiv 2203.01752 ↗First posted 3 Mar 2022cs.LG cs.DCcited by 9

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Abstract (arXiv)

Despite enormous research interest and rapid application of federated learning (FL) to various areas, existing studies mostly focus on supervised federated learning under the horizontally partitioned local dataset setting. This paper will study the unsupervised FL under the vertically partitioned dataset setting. Accordingly, we propose the federated principal component analysis for vertically partitioned dataset (VFedPCA) method, which reduces the dimensionality across the joint datasets over all the clients and extracts the principal component feature information for downstream data analysis. We further take advantage of the nonlinear dimensionality reduction and propose the vertical federated advanced kernel principal component analysis (VFedAKPCA) method, which can effectively and collaboratively model the nonlinear nature existing in many real datasets. In addition, we study two communication topologies. The first is a server-client topology where a semi-trusted server coordinates the federated training, while the second is the fully-decentralized topology which further eliminates the requirement of the server by allowing clients themselves to communicate with their neighbors. Extensive experiments conducted on five types of real-world datasets corroborate the efficacy of VFedPCA and VFedAKPCA under the vertically partitioned FL setting. Code is available at: https://github.com/juyongjiang/VFedPCA-VFedAKPCA

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 11 of its 38 references that are in the map. First posted on arXiv 3 Mar 2022.

Builds on

11 of 38 references.

References · 11

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  3. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  4. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  5. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 67
  6. 2020Ternary Compression for Communication-Efficient Federated Learningcited by 57
  7. 2019Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphscited by 34
  8. 2020Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospectivecited by 27
  9. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
  10. 2018Secure Federated Transfer Learningcited by 13
  11. 2020Communication-Efficient Distributed SVD via Local Power Iterationscited by 12

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Cited by year

Citing papers per year: 2022: 4; 2023: 2; 2024: 2; 2025: 1.

9 papers.

  1. 2022Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspacescited by 36
  2. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  3. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  4. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  5. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  6. 2024Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Reviewcited by 6
  7. 2025UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaborationcited by 5
  8. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  9. 2022Vertical Federated Linear Contextual Banditscited by 0