Federated Cooperative Generalized Linear Model for Distributed Multimodal Data Analysis
提出一种广义线性模型，用于分布式多模态数据，各站点本地建模后聚合，解决数据共享限制下的协同分析问题，并在帕金森病严重程度预测中验证。
We propose a generalized linear model for distributed multimodal data, where each sample contains multiple data modalities, each collected by an instrument. Unlike the centralized methods that require access to all samples, our approach assumes that the samples are distributed among several sites, and pooling the data is not allowable due to data sharing constraints. Our approach constructs a set of local predictive models based on available multimodal data at each site. Next, the local models are sent to an aggregator that constructs an aggregated model. The models are obtained by minimizing local and aggregated objective functions that include penalty terms to create consensus among the data modalities and the local sites. Through simulations, we compare the performance of the proposed method to several benchmarks. Furthermore, we assess the proposed framework for predicting the severity of Parkinson’s disease based on the patient’s activity data collected by the mPower application.