  Partially supervised segmentation is a label-saving method based on datasets
with fractional classes labeled and intersectant. However, it is still far from
landing on real-world medical applications due to privacy concerns and data
heterogeneity. As a remedy without privacy leakage, federated partially
supervised segmentation (FPSS) is formulated in this work. The main challenges
for FPSS are class heterogeneity and client drift. We propose a Unified
Federated Partially-labeled Segmentation (UFPS) framework to segment pixels
within all classes for partially-annotated datasets by training a totipotential
global model without class collision. Our framework includes Unified Label
Learning and sparsed Unified Sharpness Aware Minimization for unification of
class and feature space, respectively. We find that vanilla combinations for
traditional methods in partially supervised segmentation and federated learning
are mainly hampered by class collision through empirical study. Our
comprehensive experiments on real medical datasets demonstrate better
deconflicting and generalization ability of UFPS compared with modified
methods.
