Federated generalized linear mixed model based on one-time shared summary statistics

Music City Center 

Data privacy has increasingly become a daunting challenge because it limits data availability, which is essential in estimating statistical models such as generalized linear models. Access to personal data often involves considerable time, effort, and paperwork, which can impede research progress and collaboration. Federated learning has emerged as a means to estimate models without accessing individual observations from multiple data providers like hospitals or health organizations. However, this strategy requires communicating parameter estimate updates to a central server until convergence to produce a global model. In this research, we propose an approach to estimate mixed linear, logistic, and Poisson models based on summary statistics requested only once from each data provider. Our strategy involves generating pseudo-data whose summary statistics match those of the actual but unavailable data and using them in the model estimation process. The estimates we achieve are identical or at least as good as those derived from the actual data in terms of bias and coverage. Generalizability and communication efficiency distinguish our approach from the existing methods.

federated learning

mixed effects models

data privacy

pseudo-data

aggregate data

statistical sufficiency 

Biometrics Section