Marginal and training-conditional guarantees in one-shot federated conformal prediction

arXiv:2405.12567v1 Announce Type: cross
Abstract: We study conformal prediction in the one-shot federated learning setting. The main goal is to compute marginally and training-conditionally valid prediction sets, at the server-level, in only one round of communication between the agents and the server. Using the quantile-of-quantiles family of estimators and split conformal prediction, we introduce a collection of computationally-efficient and distribution-free algorithms that satisfy the aforementioned requirements. Our approaches come from theoretical results related to order statistics and the analysis of …

abstract agents arxiv communication compute family federated learning math.st prediction quantile server split stat.ml stat.th study training type