Federated Explainable AI Framework for Cross-Domain Medical Image Analysis
DOI:
https://doi.org/10.64137/31079458/IJCSEI-V2I2P104Keywords:
Federated Learning, Explainable Artificial Intelligence (XAI), Medical Image Analysis, Domain Adaptation, Deep Learning Privacy, Trustworthy AIAbstract
The integration of deep learning architectures into clinical workflows has catalyzed unprecedented advancements in automated medical image analysis. However, the deployment of these centralized models faces severe impediments due to data privacy mandates, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), alongside the pervasive "black-box" nature of deep neural networks. To reconcile the tension between collaborative machine learning, data sovereignty, and clinical interpretability, this paper introduces a novel Federated Explainable Artificial Intelligence (Fed-XAI) framework tailored for cross-domain medical image analysis. The proposed architecture enables multi-institutional collaboration by training robust deep learning models locally across heterogeneous healthcare domains without centralizing raw patient data. To overcome the specific challenge of domain shift—arising from variations in imaging protocols, manufacturer hardware, and patient demographics—we incorporate an adaptive, domain-agnostic aggregation protocol alongside localized feature alignment layers. Crucially, the framework embeds post-hoc interpretability mechanisms, utilizing federated gradient-based attribution and attention map aggregation, to provide clinicians with transparent, pixel-level justifications for automated diagnostic outputs. We evaluate our Fed-XAI framework across a multi-institutional dataset consisting of chest X-rays, histopathology slides, and magnetic resonance imaging (MRI) scans distributed across four simulated distinct hospital domains. The empirical results demonstrate that our framework achieves diagnostic performance metrics comparable to centralized training paradigms while maintaining strict privacy boundaries. Furthermore, the qualitative and quantitative evaluations of the generated explanations verify that the framework identifies genuine pathological biomarkers rather than exploiting spurious domain-specific artifacts, thereby establishing a verifiable foundation of trust for clinical decision support systems.
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