This talk studies conformal prediction in the setting of transfer learning, where the data in the target domain is limited but some auxiliary datasets from related source tasks are available. We propose a general framework to form prediction intervals by pooling the related data, and demonstrate the desired coverage as well as sample efficiency by applying this framework to both the standard split conformal and conformalized quantile regression (CQR) methods. Moreover, a novel meta-combination method for predictive intervals is proposed when there are communication constraints on the source tasks. We demonstrate the superiority of this approach through thorough experiments and real data applications in health care. This is joint work with Xiaotian Hou, Peng Wang and Minge Xie.