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.
@article{arxiv.2410.14707,
title = {FACMIC: Federated Adaptative CLIP Model for Medical Image Classification},
author = {Yihang Wu and Christian Desrosiers and Ahmad Chaddad},
journal= {arXiv preprint arXiv:2410.14707},
year = {2024}
}