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.

翻译：基础模型（FMs）已在多种视觉任务中展现出强大的泛化能力。然而，其在联邦学习场景中的部署受到高计算需求、大量通信开销以及显著推理成本的制约。本文提出DSFedMed，一种双尺度联邦学习框架，通过中央基础模型与轻量客户端模型之间的互知识蒸馏实现医学图像分割。为支持知识蒸馏，我们生成一组高质量医学图像以替代真实公共数据集，并提出一种可学习性引导的样本选择策略，以提升双尺度蒸馏的效率和效果。这种互蒸馏机制使得基础模型能够向轻量客户端传递通用知识，同时整合客户端特定洞察以优化基础模型。在五个医学图像分割数据集上的评估表明，与现有联邦基础模型基线相比，DSFedMed在Dice分数上平均提升2%，同时将通信成本和推理时间降低近90%。这些结果证明了该方法在资源受限的联邦部署中具有显著的效率优势和可扩展性。