Federated Multi-view Learning for Private Medical Data Integration and Analysis
العنوان: | Federated Multi-view Learning for Private Medical Data Integration and Analysis |
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المؤلفون: | Sicong Che, Zhaoming Kong, Hao Peng, Lichao Sun, Alex Leow, Yong Chen, Lifang He |
المصدر: | ACM Transactions on Intelligent Systems and Technology. 13:1-23 |
بيانات النشر: | Association for Computing Machinery (ACM), 2022. |
سنة النشر: | 2022 |
مصطلحات موضوعية: | Artificial Intelligence, Theoretical Computer Science |
الوصف: | 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. |
تدمد: | 2157-6912 2157-6904 |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::533aafff74ffe208ce8c970abc6658d1 https://doi.org/10.1145/3501816 |
رقم الأكسشن: | edsair.doi...........533aafff74ffe208ce8c970abc6658d1 |
قاعدة البيانات: | OpenAIRE |
تدمد: | 21576912 21576904 |
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