KbFL-XAI: Explainable knowledge-based federated learning for eye disease diagnosis

Abstract

Eye diseases such as cataracts, glaucoma, macular degeneration, and diabetic retinopathy significantly impair vision and quality of life, particularly in aging populations, and pose substantial socio-economic challenges. Accurate and timely diagnosis is crucial for mitigating their impact. Deep learning presents a promising solution by leveraging unlabeled data to extract meaningful features and reduce dependence on extensively labeled datasets. However, conventional deep learning models rely on centralized data collection, raising serious concerns about data security and patient privacy. Federated Learning addresses these challenges by enabling collaborative model training across multiple entities without requiring data sharing or ensuring privacy preservation. Our approach integrates EfficientNetB3 as the backbone with Residual Channel Attention and a custom classification head, achieving 94.79% accuracy. Explainable Artificial Intelligence enhances interpretability and transparency. The integration of the model into real-time diagnostic systems holds the potential for advancing clinical applications while maintaining data security and scalability.

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