FLMMIF: privacy-preserving federated multi-modal medical image fusion

FedSCF: sensitivity-aware collaborative fusion for personalized federated learning in medical image classification

A PFL framework, FedSCF, which models client heterogeneity at the parameter level, including a relative perturbation-based sensitivity evaluation is designed to identify critical parameters for personalized modeling, while the remaining parameters participate in cross-client sharing.

A Divergence-Aware Personalized Federated Transformer Framework for Privacy-Preserving Multi-Center Medical Image Intelligence

In a non-IID medical imaging scenario, the problems that occur in federated learning (FL) include high data variability, data leakage, convergence instability, and suboptimal global aggregation. The number of medical imaging applications is vast, and Federated Learning (FL) has already been used in many of them; some main challenges are the large variability in data, the potential for leakage of privacy, convergence instability, and suboptimal global aggregation in non-IID scenarios. Current adaptive aggregation strategies are heuristic optimizer switching, do not take advantage of representation learning to transform the inputs, and are not able to personalize it in a divergence-aware way. This paper will present DAP-FedTrans, a Divergence-Aware Personalized Federated Transformer framework that will be used to conduct privacy-preserving multi-center medical image intelligence. This framework quantifies the statistical heterogeneity with Jensen-Shannon divergence, gradient similarity, and Wasserstein feature distance for the purpose of dynamically partitioning the clients and providing the aggregation per cluster. The classification heads are specialized for institutions, and the universal Vision Transformer encoder is used to encourage generalization. The privacy guarantees are augmented with secure aggregation and adaptive differential privacy. The accuracy is 97.84%; F1 is 0.968, and the loss of communication is 33% in the extreme non-IID case. The outcomes reveal increased convergence stability, equity, and scalability, making DAP-FedTrans a possible paradigm for the collaborative implementation of AI-based healthcare.

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.

FedDP-PALD: A Privacy-Preserving Federated Latent Diffusion Framework with Prototype Aggregation for Medical Data Synthesis

Medical images and physiological signals provide valuable information for accurate diagnosis. Developing diagnostic models often requires patient data from multiple institutions, although strict privacy regulations limit the sharing of sensitive clinical records. Federated learning enables multiple hospitals to train a shared model without exchanging raw data. However, existing methods face two problems: the information exchanged during training can reveal whether a patient's data were used, and synthetic data meant to replace real records often fail to preserve their predictive structure, which limits clinical use. To address this issue, we propose FedDP-PALD, a privacy-preserving federated latent diffusion framework for multimodal medical data synthesis under formal privacy guarantees. It jointly processes chest X-ray images and electrocardiogram (ECG) signals through gated multi-head attention with modality-availability masks, remaining effective even when a modality is missing. We also introduce Differentially Private Prototype Mixture Aggregation (DP-PMA), which clips class-level latent prototypes and adds calibrated Gaussian noise before combining them on the server to maintain $(\epsilon, \delta)$ differential privacy. We evaluate FedDP-PALD on PneumoniaMNIST, ChestMNIST, and MIT-BIH datasets, where differential privacy reduced summary-level attack AUROC from 0.6229 $\pm$ 0.0026 to between 0.5016 and 0.5093 for privacy budgets from $\epsilon = 1$ to $\epsilon = 8$. On the test data, synthetic-latent training achieved an F1 score of 0.8993 $\pm$ 0.0006 and an AUROC of 0.9057 $\pm$ 0.0503, close to the 0.9747 $\pm$ 0.0132 real-latent training. These results show that FedDP-PALD generates private synthetic representations that preserve useful decision performance while strongly resisting membership inference.

PPFedKD: Privacy‐Preserving Federated Learning and Adaptive Contrastive Distillation for Medical Image Diagnosis

Experimental results on diabetic retinopathy and breast cancer pathology datasets demonstrate that PPFedKD outperforms baseline methods in classification accuracy, privacy protection, and communication efficiency, providing a secure and effective solution for medical image classification.

Federated learning for medical image classification based on prototype alignment