Intelligent Medical Image Analysis Using Secure and Explainable Federated Deep Learning

Privacy-Aware Adversarial Defense Approach for Medical Image Classification in Distributed Healthcare Systems

With the rapid development of the cloud computing and Internet of Things (IoT) technologies, the massive deployment of large-scale data processing systems has become possible, especially in the healthcare field where medical image analysis is used. Deep learning models have shown impressive results in diagnostic tasks, but their application in cloud-based systems introduces key privacy and security issues, such as being susceptible to adversarial attacks. Adversarial perturbations can fool classification models, leading to misdiagnosis in medicine, while the sharing and handling of personal patient information can expose the healthcare system to privacy violations. To overcome such challenges, this paper suggests a hybrid secure inference system that combines adversarial example detection with homomorphic encryption-based privacy preservation. The proposed solution is a rather light convolutional neural network (CNN) for the detection of adversarially manipulated inputs and a denoising process to reduce the impact of perturbations prior to the classification stage. The clean or restored images are then secured by means of the CKKS homomorphic encryption scheme, which allows for computing on encrypted data without exposing sensitive information. The images are then encrypted and fed through a deep neural network to classify them in a privacy-preserving manner. Experimental results on a dataset of brain tumor images show the effectiveness of the proposed framework. The model outperforms a baseline CNN model in adversarial and clean conditions with 94.4% classification accuracy, compared with the 71.1% accuracy the baseline CNN model had under adversarial conditions. The results support that the proposed framework has succeeded in providing better adversarial robustness while preserving data privacy, which is acceptable in cloud-IoT environments for secure medical image analysis.

Regulatory-orientedDeep federated learning framework for multi-hospital medical imaging: privacy-preserving, explainable, and generalizable diagnosis

Medical imaging has been transformed by Artificial Intelligence (AI) and Deep Learning (DL). Yet, multi-hospital deployment remains limited by patient privacy concerns, heterogeneous data distributions, and insufficient model interpretability, which affect regulatory approval and clinical trust. This study proposes a regulatory-grade Federated Learning (FL) framework for secure, interpretable, and generalizable collaborative medical imaging. The proposed framework integrates Slicing Window Adaptive Kalman Filtering (SWAKF) for image denoising, Structured Multi-Modal Autoencoder Attention Fusion (SMAAF) for feature representation, and adaptive federated aggregation to address non-IID data across hospitals. Patient privacy is preserved using secure aggregation, differential privacy, and encryption, while Grad-CAM, SHAP, and LIME provide model interpretability. The proposed framework outperformed Vision Transformer, AlexNet, FedAvg, and FedProx on Brain Tumor and Alzheimer's MRI datasets. It achieved 96.1% accuracy F1-score 96.1%, and 0.978 for Brain Tumor classification, and 94.8% accuracy and 0.968 for Alzheimer's classification. The framework also reduced calibration error, exhibited minimal encryption overhead, maintained robustness under noisy-label and non-IID conditions, and demonstrated statistically significant improvements p < 0.01 over baseline methods. The proposed FL framework provides a privacy-preserving, explainable, and computationally efficient solution for collaborative AI in medical imaging. By combining adaptive federated learning, secure privacy mechanisms, and explainable AI techniques, it improves diagnostic performance while supporting regulatory compliance and clinical trust, demonstrating strong potential for deployment in multi-hospital clinical environments.

Federated Explainable AI Framework for Cross-Domain Medical Image Analysis

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.

Federated learning explainable deep learning framework for enhanced internet of medical things security

The Internet of Medical Things (IoMT) enables remote patient monitoring, providing timely diagnostic assessments crucial for saving lives. Despite these benefits, IoMT systems face significant security challenges that endanger patient privacy and data integrity. Existing centralized machine learning (ML) and deep learning (DL) solutions, while useful, are “black boxes” that raise critical concerns about data privacy and ethical compliance, as sharing sensitive patient information in a centralized system may violate confidentiality, integrity, and availability, particularly in healthcare sectors where patient data is highly confidential. Motivated by these challenges, we propose a novel federated learning (FL) explainable deep recurrent neural network (DRNN)-augmented long short-term memory (LSTM) framework for anomaly intrusion in IoMT systems, named federated recurrent short-term memory. The proposed decentralized framework processes data on local devices, sharing only learned model weights with the central server to safeguard data integrity. Training the DRNN-LSTM presents a non-convex optimization problem that is computationally expensive. To address this issue, we propose a new Bayesian Optimization (BO) method, which models the objective function stochastically and intelligently selects the most promising hyperparameter configurations to identify the hyperparameter vector that maximizes the objective function. The Adam optimizer was implemented to address gradient descent resource issues and improve the speed of convergence with a learning rate of 0.001. We introduced Shapley Additive Explanations (XAI) to ensure security decisions are transparent, a key advantage over existing black-box approaches that helps meet ethical guidelines such as HIPAA and GDPR. The framework was evaluated on the Washington University in St. Louis Enhanced Healthcare Monitoring System (WUSTL-EHMS), as well as the new CIC-IoMT2024 (IoMT-specific) and BoT-IoT (general IoT) benchmark datasets. The results show strong detection capabilities, with high detection rates for critical attack types such as DoS with 99.94%, DoS SYN with 100%, and DoS TCP with 100%. The model also demonstrated exceptionally high precision for classes like DoS SYN, DDoS TCP, and ARP spoofing. Our proposed federated recurrent short-term memory framework achieves a robust balance between detection fidelity and computational efficiency, making it a reliable solution for intrusion detection in resource-constrained IoMT infrastructures.

Federated learning for medical image classification based on prototype alignment

A Federated Learning Framework for Brain Tumour Detection Using Xception Convolutional Neural Network with CKKS-Based Secure Aggregation