Privacy Aware Collaborative Inference for GLMs

Thomas M. Menino Convention & Exhibition Center 

With sensitive data collected across various sites, restrictions on data sharing can hinder statistical inference. Recent works in collaborative iterative algorithms like Federated Learning (FL) have demonstrated methods to perform statistical inference with various classical models under this setup. These tasks present a unique challenge of accounting for both the inherent statistical uncertainty of the estimators and the numerical convergence of the iterates. To the best of our knowledge, theoretical analyses till date have considered either online learning frameworks or implicit algorithms for learning. However, in several real life applications, models can only be trained on limited data collected a priori. On the other hand, implicit algorithms incur additional computational cost and generalization gap relative to their explicit counterparts such as stochastic gradient descent (SGD) at each local iteration. In this work, we explore the combined uncertainty of FL iterates under offline learning with limited data, based on the SGD optimization routine. We explore their small sample properties as well as asymptotic behavior, extending them to a collaborative inference framework.

Federated Learning

Inference

Small sample properties

Asymptotics

Offline learning

Variance 

Section on Statistical Learning and Data Science