Distributed Sequential Federated Estimation

When analyzing data stored across multiple sites, concerns about data security and communication arise.Federated learning, which avoids centralizing data, offers a p r omising s o lution t o a d dress t h ese c o ncerns.H o wever, integrating information from separate local sites in a statistically sound manner is crucial, as common averaging methods may lead to information loss due to data non-homogeneity and incomparable results among sites.By applying sequential methods in federated learning, integration can be facilitated and the analysis process can be accelerated, particularly within a distributed computing framework.We propose an efficient da ta -driven me th od th at ma intains th e principles of classical sequential adaptive design.Numerical studies and an application to COVID-19 data from 32 hospitals in Mexico, using a regression model, illustrate the effectiveness o f o u r approach.