  Developing a generalized segmentation model capable of simultaneously
delineating multiple organs and diseases is highly desirable. Federated
learning (FL) is a key technology enabling the collaborative development of a
model without exchanging training data. However, the limited access to fully
annotated training data poses a major challenge to training generalizable
models. We propose "ConDistFL", a framework to solve this problem by combining
FL with knowledge distillation. Local models can extract the knowledge of
unlabeled organs and tumors from partially annotated data from the global model
with an adequately designed conditional probability representation. We validate
our framework on four distinct partially annotated abdominal CT datasets from
the MSD and KiTS19 challenges. The experimental results show that the proposed
framework significantly outperforms FedAvg and FedOpt baselines. Moreover, the
performance on an external test dataset demonstrates superior generalizability
compared to models trained on each dataset separately. Our ablation study
suggests that ConDistFL can perform well without frequent aggregation, reducing
the communication cost of FL. Our implementation will be available at
https://github.com/NVIDIA/NVFlare/tree/dev/research/condist-fl.
