  Federated learning (FL) for histopathology image segmentation involving
multiple medical sites plays a crucial role in advancing the field of accurate
disease diagnosis and treatment. However, it is still a task of great
challenges due to the sample imbalance across clients and large data
heterogeneity from disparate organs, variable segmentation tasks, and diverse
distribution. Thus, we propose a novel FL approach for histopathology nuclei
and tissue segmentation, FedSODA, via synthetic-driven cross-assessment
operation (SO) and dynamic stratified-layer aggregation (DA). Our SO constructs
a cross-assessment strategy to connect clients and mitigate the representation
bias under sample imbalance. Our DA utilizes layer-wise interaction and dynamic
aggregation to diminish heterogeneity and enhance generalization. The
effectiveness of our FedSODA has been evaluated on the most extensive
histopathology image segmentation dataset from 7 independent datasets. The code
is available at https://github.com/yuanzhang7/FedSODA.
