Towards Clinically Applicable Large-Model-Based Privacy-Preserving Polyp Segmentation: A Federated LoRA Approach to Colonoscopy

Colonoscopy polyp segmentation is essential for accurate lesion detection and workflow optimization in clinical practice. However, deploying large foundation models in medical settings faces challenges related to patient privacy, computational overhead, and heterogeneous data distributions. In this study, we propose PolypSAMFL, a novel framework that integrates lowrank adaptation (LoRA) into the Segment Anything Model (SAM) within a federated learning paradigm to deliver privacypreserving, highprecision polyp segmentation. By freezing the majority of SAM's pretrained parameters and finetuning only compact LoRA modules in the image encoder and mask decoder, PolypSAMFL significantly reduces communication costs while maintaining robust feature extraction across distributed clinical datasets. We further propose a boundaryaware loss function and a multiresolution mask synthesis strategy to enhance delineation of irregular and lowcontrast polyp boundaries. Extensive evaluation on four public colonoscopy datasets demonstrates that our method yields a mean Dice score of 0.987 and intersectionoverunion of 0.976, outperforming stateoftheart approaches while fully preserving data locality. This translates directly to more reliable identification of polyp during colonoscopy. These results validate the clinical utility of PolypSAMFL for realworld, AIdriven colonoscopy workflows, offering a scalable solution that aligns with privacy regulations and resource constraints in modern healthcare environments.