Collaborative Learning Amidst Heterogeneity

Music City Center 

In this talk, we explore statistical challenges and opportunities in collaborative learning under extreme heterogeneity.

First, we study the question of how collaborative learning can be used to causal inference beyond meta-analysis and introduce a novel collaborative inverse propensity score weighting estimator. Our approach demonstrates significant improvements over existing methods, especially as heterogeneity increases.

Then, we re-examine optimal experiment design from a multi-agent perspective, formulating the tension between a global federated learning platform and local data contributors as a game. We show that this perspective sheds new insights on the classical question of which optimality criterion should we use.

federated learning

causal inference

experiment design

mechanism design

collaborative learning

meta analysis