  Medical image segmentation is crucial for clinical diagnosis. The
Segmentation Anything Model (SAM) serves as a powerful foundation model for
visual segmentation and can be adapted for medical image segmentation. However,
medical imaging data typically contain privacy-sensitive information, making it
challenging to train foundation models with centralized storage and sharing. To
date, there are few foundation models tailored for medical image deployment
within the federated learning framework, and the segmentation performance, as
well as the efficiency of communication and training, remain unexplored. In
response to these issues, we developed Federated Foundation models for Medical
image Segmentation (FedFMS), which includes the Federated SAM (FedSAM) and a
communication and training-efficient Federated SAM with Medical SAM Adapter
(FedMSA). Comprehensive experiments on diverse datasets are conducted to
investigate the performance disparities between centralized training and
federated learning across various configurations of FedFMS. The experiments
revealed that FedFMS could achieve performance comparable to models trained via
centralized training methods while maintaining privacy. Furthermore, FedMSA
demonstrated the potential to enhance communication and training efficiency.
Our model implementation codes are available at
https://github.com/LIU-YUXI/FedFMS.
