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.

本论文提出了一个基于联合多视图学习的框架，用于多视图数据泄露预防，以解决医学数据分布困难、数据来源异构等问题。该框架兼容垂直联合多视图学习和水平联合多视图学习两种问题，并通过键盘数据的实验验证表明，该方法可以在保护隐私的同时充分利用多视图数据，同时 V-FedMV 和 H-FedMV 方法的表现都显著好于其单视图和成对对应方法。此框架是首个考虑在联合多视图设置中进行垂直和水平多样化以及顺序联合学习的研究。