Few-shot learning which aims to generalize knowledge learned from annotated base training data to recognize unseen novel classes has attracted considerable attention. Existing few-shot methods rely on completely clean training data. However, in the real world, the training data are always corrupted and accompanied by noise due to the disturbance in data transmission and low-quality annotation, which severely degrades the performance and generalization capability of few-shot models. To address the problem, we propose a unified peer-collaboration learning (PCL) framework to extract valid knowledge from corrupted data for few-shot learning. PCL leverages two modules to mimic the peer collaboration process which cooperatively evaluates the importance of each sample. Specifically, each module first estimates the importance weights of different samples by encoding the information provided by the other module from both global and local perspectives. Then, both modules leverage the obtained importance weights to guide the reevaluation of the loss value of each sample. In this way, the peers can mutually absorb knowledge to improve the robustness of few-shot models. Experiments verify that our framework combined with different few-shot methods can significantly improve the performance and robustness of original models.