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

arXiv: https://arxiv.org/abs/2105.01603 · HTML: https://mlsanity.com/paper/2105.01603/

## 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 (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records](https://mlsanity.com/paper/1903.09296/) (2019)
- [Multi-Participant Multi-Class Vertical Federated Learning](https://mlsanity.com/paper/2001.11154/) (2020)
- [Federated Multi-view Matrix Factorization for Personalized Recommendations](https://mlsanity.com/paper/2004.04256/) (2020)
- [Asymmetrical Vertical Federated Learning](https://mlsanity.com/paper/2004.07427/) (2020)
- [Federated Tensor Factorization for Computational Phenotyping](https://mlsanity.com/paper/1704.03141/) (2017)
- [Efficient Machine Learning for Big Data: A Review](https://mlsanity.com/paper/1503.05296/) (2015)
- [Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data](https://mlsanity.com/paper/1910.12191/) (2019)
- [A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations](https://mlsanity.com/paper/2008.10808/) (2020)
- [DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection](https://mlsanity.com/paper/1803.08986/) (2018)
- [A Self-Organizing Tensor Architecture for Multi-View Clustering](https://mlsanity.com/paper/1810.07874/) (2018)

## Built on by (most cited first, 12 of 12)

- [FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning](https://mlsanity.com/paper/2206.03200/) (2022)
- [One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis](https://mlsanity.com/paper/2207.06509/) (2022)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources](https://mlsanity.com/paper/2305.06217/) (2023)
- [Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees](https://mlsanity.com/paper/2209.01539/) (2022)
- [Federated Deep Multi-View Clustering with Global Self-Supervision](https://mlsanity.com/paper/2309.13697/) (2023)
- [UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration](https://mlsanity.com/paper/2501.11388/) (2025)
- [Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis](https://mlsanity.com/paper/2410.16870/) (2024)
- [Active-Passive Federated Learning for Vertically Partitioned Multi-view Data](https://mlsanity.com/paper/2409.04111/) (2024)
- [Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views](https://mlsanity.com/paper/2410.09484/) (2024)
- [Enhanced Federated Deep Multi-View Clustering under Uncertainty Scenario](https://mlsanity.com/paper/2511.17631/) (2025)

## 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.