Radiotherapy treatment planning takes substantial time, several hours per patient, as it involves manual segmentation of tumor and risk organs. Segmentation networks can be trained to automatically perform the segmentations, but typically require large annotated datasets for training. Sharing of sensitive data between hospitals, to create a larger dataset, is often difficult due to ethics and GDPR. Here we therefore demonstrate that federated learning is a solution to this problem, as then only the segmentation model is sent between each hospital and a global server. We export and preprocess brain tumor images from the oncology departments in Linköping and Lund, and use federated learning to train a global segmentation model using two different frameworks.