Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation
This addresses initialization challenges in federated learning for medical image analysis, offering a novel but incremental approach.
The paper tackles the problem of non-IID data in federated learning for medical image segmentation by proposing foundation model instructed initialization instead of random initialization, showing that it achieves faster convergence and improved performance on chest x-ray lung segmentation.
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.