bibtext: @InProceedings{ Wu_FACMIC_MICCAI2024,
                   author = { Wu, Yihang and Desrosiers, Christian and Chaddad, Ahmad },
                   title = { { FACMIC: Federated Adaptative CLIP Model for Medical Image Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15012 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 \url{https://github.com/AIPMLab/FACMIC}.
title: FACMIC: Federated Adaptative CLIP Model for Medical Image Classification
authors: Wu, Yihang Desrosiers, Christian Chaddad, Ahmad