  In medical image analysis, Federated Learning (FL) stands out as a key
technology that enables privacy-preserved, decentralized data processing,
crucial for handling sensitive medical data. Currently, most FL models employ
random initialization, which has been proven effective in various instances.
However, given the unique challenges posed by non-IID (independently and
identically distributed) data in FL, we propose a novel perspective: exploring
the impact of using the foundation model with enormous pre-trained knowledge,
such as the Segment Anything Model (SAM), as an instructive teacher for FL
model initialization in medical image segmentation task. This work for the
first time attempts to utilize the foundation model as an instructive teacher
for initialization in FL, assessing its impact on the performance of FL models,
especially in non-IID data scenarios. Our empirical evaluation on chest x-ray
lung segmentation showcases that FL with foundation model instructed
initialization not only achieves faster convergence but also improves
performance in complex data contexts. These findings offer a new perspective
for model initialization in FL.
