Decentralized LoRA augmented transformer with multi-scale feature learning for secured eye diagnosis

Privacy-preserving diagnosis is achievable through federated learning: The paper demonstrates that eye disease diagnosis can be performed using decentralized training without compromising patient data privacy, addressing a major concern in medical AI deployment where sensitive health information must be protected.

Smaller, more efficient models can match larger ones: By using Low-Rank Adaptation (LoRA), the framework reduces the number of trainable parameters while maintaining or improving diagnostic accuracy on benchmark datasets (OCTDL and Eye Disease Image Dataset), making AI tools more practical for resource-limited healthcare settings.

AI interpretability builds clinical trust: The use of Grad-CAM++ visualizations to explain model predictions supports clinical adoption by showing doctors why the AI made specific diagnostic recommendations, rather than treating it as a "black box."

Multi-scale feature learning addresses real clinical challenges: The framework tackles practical problems in medical imaging including data imbalance and limited training data through multi-scale patch analysis and knowledge distillation, making the approach more robust for real-world deployment.

Responsible AI design requires integrated solutions: Rather than addressing privacy, efficiency, and interpretability separately, this work shows these concerns are best tackled together—combining federated learning, parameter efficiency, and explainability in a single framework for trustworthy medical AI.