Title: Federated Learning with Openset Noisy Labels. Abstract: Federated learning is a learning paradigm that allows the central server to learn from different data sources while keeping the data private at local. Without controlling and monitoring the local data collection process, it is highly likely that the locally available training labels are noisy, just as in a centralized data collection effort. Moreover, different clients may hold samples within different label spaces. The noisy label space is likely to be different from the unobservable clean label space, resulting in openset noisy labels. In this work, we study the challenge of federated learning from clients with openset noisy labels. We observe that many existing solutions, e.g., loss correction, in the noisy label literature cannot achieve their originally claimed effect in local training. A central contribution of this work is to propose an approach that communicates globally randomly selected ``contrastive labels" among clients to prevent local models from memorizing the openset noise patterns individually. Randomized label generations are applied during label sharing to facilitate access to the contrastive labels while ensuring differential privacy (DP). Both the DP guarantee and the effectiveness of our approach are theoretically guaranteed. Compared with several baseline methods, our solution shows its efficiency in several public benchmarks and real-world datasets under different noise ratios and noise models. 