*Result*: Federated Cooperative Generalized Linear Model for Distributed Multimodal Data Analysis.

*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. [ABSTRACT FROM AUTHOR]

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