Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification

The introduction of FedMedCLIP marks a pivotal development in medical image classification, addressing critical challenges such as privacy concerns and data heterogeneity through federated learning. This decentralized approach allows multiple hospitals to collaboratively train a model without sharing sensitive data, thus enhancing privacy. The model employs a contrastive language-image pre-training technique, along with a masked feature adaptation module and a masked multi-layer perceptron, to optimize resource efficiency and reduce computational overhead. Notably, FedMedCLIP has demonstrated an 8% performance improvement over the second-best baseline on the ISIC2019 dataset, underscoring its potential to revolutionize medical imaging practices while maintaining patient confidentiality.