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Partially supervised segmentation is a label-saving method based on datasetswith fractional classes labeled and intersectant. However, it is still far fromlanding on real-world medical applications due to privacy concerns and dataheterogeneity. As a remedy without privacy leakage, federated partiallysupervised segmentation (FPSS) is formulated in this work. The main challengesfor FPSS are class heterogeneity and client drift. We propose a UnifiedFederated Partially-labeled Segmentation (UFPS) framework to segment pixelswithin all classes for partially-annotated datasets by training a totipotentialglobal model without class collision. Our framework includes Unified LabelLearning and sparsed Unified Sharpness Aware Minimization for unification ofclass and feature space, respectively. We find that vanilla combinations fortraditional methods in partially supervised segmentation and federated learningare mainly hampered by class collision through empirical study. Ourcomprehensive experiments on real medical datasets demonstrate betterdeconflicting and generalization ability of UFPS compared with modifiedmethods.

UFPS: A unified framework for partially-annotated federated segmentation in heterogeneous data distribution