DSFedMed: 通过基础模型与轻量模型之间的相互蒸馏实现双尺度联邦医疗图像分割
DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models
AI总结：
DSFedMed通过基础模型与轻量模型之间的相互蒸馏，在医疗图像分割中实现双尺度联邦学习，提升效率与可扩展性。
摘要：
基础模型（FMs）在多样视觉任务中表现出强大的泛化能力。然而，它们在联邦设置中的部署受到高计算需求、显著的通信开销和较大的推理成本的阻碍。我们提出了DSFedMed，一种双尺度联邦框架，该框架使中央基础模型与轻量客户端模型之间能够相互进行知识蒸馏。为了支持知识蒸馏，生成了一组高质量的医学图像来替代真实的公共数据集，并提出了一种基于可学习性的样本选择策略，以提高双尺度蒸馏的效率和效果。这种相互蒸馏使基础模型能够将通用知识转移到轻量客户端，同时将客户端特定的见解整合到基础模型中。在五个医学图像分割数据集上的评估显示，DSFedMed在Dice分数上平均提高了2个百分点，同时与现有联邦基础模型基线相比，通信成本和推理时间分别减少了近90个百分点。这些结果展示了在资源受限的联邦部署中显著的效率提升和可扩展性。
英文摘要：
Foundation Models (FMs) have demonstrated strong generalization across diverse vision tasks. However, their deployment in federated settings is hindered by high computational demands, substantial communication overhead, and significant inference costs. We propose DSFedMed, a dual-scale federated framework that enables mutual knowledge distillation between a centralized foundation model and lightweight client models for medical image segmentation. To support knowledge distillation, a set of high-quality medical images is generated to replace real public datasets, and a learnability-guided sample selection strategy is proposed to enhance efficiency and effectiveness in dual-scale distillation. This mutual distillation enables the foundation model to transfer general knowledge to lightweight clients, while also incorporating client-specific insights to refine the foundation model. Evaluations on five medical imaging segmentation datasets show that DSFedMed achieves an average 2 percent improvement in Dice score while reducing communication costs and inference time by nearly 90 percent compared to existing federated foundation model baselines. These results demonstrate significant efficiency gains and scalability for resource-limited federated deployments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
