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

By Sicong Che, Lichao Sun, Yong Chen, Lifang He. 2021

50 citations recorded by the source corpus.

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 the medical field. Two critical challenges are identified: First, medical data is naturally distributed across multiple local sites, making it difficult to collectively train machine learning models without data leakage. Second, in medical applications, data are often collected from different sources and views, resulting in heterogeneity and complexity that requires reconciliation. In this article, we present a generic Federated Multi-view Learning (FedMV) framework for multi-view data leakage prevention. Specifically, we apply this framework to two types of problems based on local data availability: 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. Our results demonstrated that the proposed approach can make full use of multi-view data in a privacy-preserving way, and both V-FedMV and H-FedMV perform better than their single-view and pairwise counterparts. Besides, the framework can be easily adapted to deal with multi-view sequential data. We have developed a sequential model (S-FedMV) that takes sequence of multi-view data as input and demonstrated it experimentally. 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.

Subfields: Artificial Intelligence, Epidemiology

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Citations in the corpus

Federated Model Distillation with Noise-Free Differential Privacy (2020, background): “Federated learning (FL) provides a privacy-aware paradigm of model training, which allows a multitude of parties to construct a joint model without directly exposing their private training data [McMahan et al., 2017; Bonawitz et al., 2017; Xu et al., 2021; Che et al., 2021].”
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy (2020, background): “, diagnosis of diseases with medical records, or genetic sequences [Xu et al., 2021; Che et al., 2021].”