  Federated learning (FL) for medical image segmentation becomes more
challenging in multi-task settings where clients might have different
categories of labels represented in their data. For example, one client might
have patient data with "healthy'' pancreases only while datasets from other
clients may contain cases with pancreatic tumors. The vanilla federated
averaging algorithm makes it possible to obtain more generalizable deep
learning-based segmentation models representing the training data from multiple
institutions without centralizing datasets. However, it might be sub-optimal
for the aforementioned multi-task scenarios. In this paper, we investigate
heterogeneous optimization methods that show improvements for the automated
segmentation of pancreas and pancreatic tumors in abdominal CT images with FL
settings.
