  Medical image classification plays a crucial role in computer-aided clinical
diagnosis. While deep learning techniques have significantly enhanced
efficiency and reduced costs, the privacy-sensitive nature of medical imaging
data complicates centralized storage and model training. Furthermore,
low-resource healthcare organizations face challenges related to communication
overhead and efficiency due to increasing data and model scales. This paper
proposes a novel privacy-preserving medical image classification framework
based on federated learning to address these issues, named FedMIC. The
framework enables healthcare organizations to learn from both global and local
knowledge, enhancing local representation of private data despite statistical
heterogeneity. It provides customized models for organizations with diverse
data distributions while minimizing communication overhead and improving
efficiency without compromising performance. Our FedMIC enhances robustness and
practical applicability under resource-constrained conditions. We demonstrate
FedMIC's effectiveness using four public medical image datasets for classical
medical image classification tasks.
