轻量级联邦CLIP模型，提升医疗图像分类隐私保护效率
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification
- 为客户端定制特征选择，降低通信开销
- 跨域适应技术缓解多源数据分布差异
- 适配真实医疗场景的分布式图像分类
联邦学习（FL）为医疗图像分析提供了隐私保护的深度模型训练方案，但通信成本是制约其性能的关键因素。由于视觉基础模型传输资源开销大，直接应用存在挑战。本文提出一种面向医疗图像分类任务的联邦自适应CLIP模型（FACMIC），引入轻量化高效特征注意力模块，根据各客户端数据特性动态选择合适特征。同时提出领域自适应技术，减少客户端间数据分布差异。在四个公开医疗图像数据集上的实验表明，FACMIC在处理现实世界多源医学影像时表现优异。代码已开源：https://github.com/AIPMLab/FACMIC。
原文摘要 · Abstract (English)
Federated learning (FL) has emerged as a promising approach to medical image analysis that allows deep model training using decentralized data while ensuring data privacy. However, in the field of FL, communication cost plays a critical role in evaluating the performance of the model. Thus, transferring vision foundation models can be particularly challenging due to the significant resource costs involved. In this paper, we introduce a federated adaptive Contrastive Language Image Pretraining CLIP model designed for classification tasks. We employ a light-weight and efficient feature attention module for CLIP that selects suitable features for each client's data. Additionally, we propose a domain adaptation technique to reduce differences in data distribution between clients. Experimental results on four publicly available datasets demonstrate the superior performance of FACMIC in dealing with real-world and multisource medical imaging data. Our codes are available at https://github.com/AIPMLab/FACMIC.
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