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

• URL: http://arxiv.org/abs/2505.06982v1
• Date: Sun, 11 May 2025 13:51:56 GMT
• Title: Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition
• Authors: Md. Naimur Asif Borno, Md Sakib Hossain Shovon, MD Hanif Sikder, Iffat Firozy Rimi, Tahani Jaser Alahmadi, Mohammad Ali Moni,
• Abstract summary: This study introduces a data-efficient image transformer (DeIT)-based approach to advance AI-powered medical imaging and disease detection.<n>It achieves state-of-the-art performance, with the highest AUC, F1 score, precision, minimal loss, and Top-5 accuracy.<n>Grad-CAM++ visualizations improve interpretability by highlighting critical pathological regions, enhancing the model's clinical relevance.
• Score: 2.1358421658740214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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