Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition
Journal:
arXiv
Published Date:
May 11, 2025
Abstract
Recent progress in image-based medical disease detection encounters
challenges such as limited annotated data sets, inadequate spatial feature
analysis, data security issues, and inefficient training frameworks. This study
introduces a data-efficient image transformer (DeIT)-based approach that
overcomes these challenges by utilizing multiscale patch embedding for better
feature extraction and stratified weighted random sampling to address class
imbalance. The model also incorporates a LoRA-enhanced transformer encoder, a
distillation framework, and federated learning for decentralized training,
improving both efficiency and data security. Consequently, it achieves
state-of-the-art performance, with the highest AUC, F1 score, precision,
minimal loss, and Top-5 accuracy. Additionally, Grad-CAM++ visualizations
improve interpretability by highlighting critical pathological regions,
enhancing the model's clinical relevance. These results highlight the potential
of this approach to advance AI-powered medical imaging and disease detection.
