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.

通过将联邦学习与知识蒸馏技术相结合，提出了一种名为ConDistFL的框架，通过合理设计的条件概率表示，从部分标注的数据中提取未标注的器官和肿瘤的知识，解决了训练可泛化模型所面临的数据访问限制的问题。在四个不同的部分标注腹部CT数据集上验证了该框架的有效性，并与基线模型FedAvg和FedOpt进行了比较，实验结果表明该框架明显优于基线模型。此外，对外部测试数据集的性能也表明，与单独训练在每个数据集上的模型相比，该框架具有较高的泛化能力。通过消减频繁聚合的需求，减少了联邦学习的通信成本。

ConDistFL: 从部分标注数据进行条件蒸馏的联邦学习