FedXAI-Med: A Federated and Explainable Deep Learning Framework for Privacy-Preserving Medical Image Diagnosis

Abstract

This paper presents FedXAI-Med, a new federated and explainable deep learning framework aimed at protecting patient privacy in medical image diagnosis. The framework uses Federated Learning (FL) to allow collaborative model training among different healthcare institutions without sharing sensitive patient data. It also incorporates Explainable Artificial Intelligence (XAI) techniques to improve model interpretability and build trust in clinical settings by offering clear explanations for diagnostic predictions. FedXAI-Med seeks to tackle two significant challenges in medical AI: data privacy and model transparency. It does this by using decentralized learning and visual interpretability methods like Grad-CAM and SHAP. The framework shows its ability to enhance diagnostic performance while ensuring compliance with regulations and promoting ethical use in real-world healthcare situations.

Introduction

Medical imaging plays a vital role in modern healthcare, and recent advances in deep learning—particularly CNNs and vision transformers—have achieved expert-level performance in disease classification, segmentation, and anomaly detection across modalities such as MRI, CT, and X-ray. However, real-world clinical adoption remains limited due to two major challenges: strict patient data privacy regulations and the lack of interpretability of deep learning models.

Medical data are decentralized across institutions and governed by regulations like HIPAA and GDPR, which restrict centralized data sharing. Federated Learning (FL) addresses this issue by enabling collaborative model training without sharing raw data, instead exchanging model parameters. While FL ensures privacy and regulatory compliance, it does not solve the problem of model transparency. Deep learning models are often perceived as “black boxes,” reducing clinician trust. Explainable Artificial Intelligence (XAI) addresses this gap by providing interpretable insights using techniques such as Grad-CAM, SHAP, and Integrated Gradients.

Most existing research treats FL and XAI separately, highlighting the need for an integrated approach. To bridge this gap, the paper proposes FedXAI-Med, a unified federated and explainable deep learning framework for privacy-preserving medical image diagnosis. The framework enables decentralized training across healthcare institutions while generating clinically meaningful explanations locally, without exposing sensitive patient data.

FedXAI-Med employs federated averaging (FedAvg) to aggregate locally trained models and incorporates XAI techniques to provide visual and quantitative explanations of predictions. The framework is evaluated on MRI, CT, and X-ray datasets, focusing on diagnostic accuracy, privacy preservation, and interpretability. Experimental results show that FedXAI-Med outperforms centralized and standard federated models, achieving high accuracy (94%), strong privacy protection (95%), and superior interpretability (90%).

Conclusion

This work presented FedXAI-Med, a novel framework that integrates federated learning with explainable artificial intelligence to enable privacy-preserving, transparent, and high-performance medical image diagnosis. By allowing multiple institutions to collaboratively train models without sharing raw patient data, the framework addresses critical healthcare challenges related to data privacy, regulatory compliance, and clinician trust, while XAI techniques such as Grad-CAM and SHAP provide meaningful visual and feature-level explanations to support clinical decision-making. Experimental results demonstrate that FedXAI-Med achieves superior diagnostic accuracy compared to centralized and standard federated approaches, while simultaneously enhancing interpretability and ethical reliability. Building on these outcomes, future work will focus on scaling the framework to larger and more heterogeneous multi-institutional datasets, integrating multimodal medical data such as imaging, EHRs, and genomics, strengthening privacy through advanced techniques like differential privacy and secure multiparty computation, and enabling real-time deployment within clinical workflows. Additionally, incorporating clinician feedback and aligning with regulatory and ethical standards will further improve usability and trust, positioning FedXAI-Med as a strong foundation for the next generation of collaborative, explainable, and privacy-aware AI systems in healthcare.

References

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Copyright

Copyright © 2025 Akshay Singh, Uday Singh Kushwaha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.