Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition

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.

@article{borno2025_2505.06982,
  title={ Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition },
  author={ Md. Naimur Asif Borno and Md Sakib Hossain Shovon and MD Hanif Sikder and Iffat Firozy Rimi and Tahani Jaser Alahmadi and Mohammad Ali Moni },
  journal={arXiv preprint arXiv:2505.06982},
  year={ 2025 }
}