mlsanityData to 14 Oct 2026

Federated Multi-View Learning for Private Medical Data Integration and Analysis

arXiv 2105.01603 ↗First posted 4 May 2021cs.AI cs.LGcited by 12

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Abstract (arXiv)

Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in medical field. Two critical challenges are identified: Firstly, medical data is naturally distributed across multiple local sites, making it difficult to collectively train machine learning models without data leakage. Secondly, in medical applications, data are often collected from different sources and views, resulting in heterogeneity and complexity that requires reconciliation. This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV). We experimented with real-world keyboard data collected from BiAffect study. The results demonstrated that the proposed FedMV approach can make full use of multi-view data in a privacy-preserving way, and both V-FedMV and H-FedMV methods perform better than their single-view and pairwise counterparts. Besides, the proposed model can be easily adapted to deal with multi-view sequential data in a federated environment, which has been modeled and experimentally studied. To the best of our knowledge, this framework is the first to consider both vertical and horizontal diversification in the multi-view setting, as well as their sequential federated learning.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 16 of its 63 references that are in the map. First posted on arXiv 4 May 2021.

Builds on

16 of 63 references.

References · 16

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4 more
  1. 2019Federated Uncertainty-Aware Learning for Distributed Hospital EHR Datacited by 17
  2. 2020A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendationscited by 13
  3. 2018DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detectioncited by 10
  4. 2018A Self-Organizing Tensor Architecture for Multi-View Clusteringcited by 2

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Cited by year

Citing papers per year: 2022: 3; 2023: 3; 2024: 4; 2025: 2.

12 papers.

  1. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  2. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  3. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  4. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  5. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  6. 2022Cross-Network Social User Embedding with Hybrid Differential Privacy Guaranteescited by 5
  7. 2023Federated Deep Multi-View Clustering with Global Self-Supervisioncited by 5
  8. 2025UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaborationcited by 5
  9. 2024Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysiscited by 3
  10. 2024Active-Passive Federated Learning for Vertically Partitioned Multi-view Datacited by 0
  11. 2024Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Viewscited by 0
  12. 2025Enhanced Federated Deep Multi-View Clustering under Uncertainty Scenariocited by 0