FedSODA: Federated Cross-assessment and Dynamic Aggregation for
  Histopathology Segmentation
Yuan Zhang Yaolei Qi Xiaoming Qi Lotfi Senhadji Yongyue Wei Feng Chen Guanyu Yang
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.