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

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.

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

Experimental results demonstrate that FLMMIF generates high-quality fusion results that effectively protect data privacy while achieving precise node-specific personalization.

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 learning for medical image classification based on prototype alignment

Federated Learning-Enabled Image Processing for Privacy-Preserving Gastrointestinal Disease Screening

Background: Gastrointestinal (GI) diseases, including colorectal cancer, gastric cancer, polyps, and inflammatory bowel disease, account for over three million deaths annually worldwide. Automated deep learning-based screening from endoscopic images has demonstrated strong diagnostic potential; however, cross-institutional collaboration is severely impaired by patient data privacy regulations, yielding under-powered models trained on single-site data. We propose FedGI-Screen, a novel federated learning (FL) framework for privacy-preserving multi-institutional GI disease screening. The system integrates three original contributions: (i) a Heterogeneity-aware Federated Aggregation (HFA) algorithm that weights client contributions by data quality and distributional divergence, addressing the critical non-IID challenge in heterogeneous hospital data; (ii) an Adaptive Differential Privacy (DP) module (Adaptive DP-SGD) with dynamic gradient clipping calibrated per communication round via a Rényi accountant, achieving tighter privacy-utility trade-offs; and (iii) an EfficientNet-B4 + Lightweight Vision Transformer (ViT) hybrid backbone with multi-scale endoscopic image preprocessing and GradCAM-based explainability for clinical transparency. Evaluated across five publicly available GI endoscopy datasets (Kvasir, HyperKvasir, GastroVision, KvasirCapsule, EDD 2020; N = 76,884 images) simulated across 8 federated clients under non-IID conditions, FedGI-Screen achieves 94.8% accuracy, 94.7% F1-score, and an AUC of 0.976— surpassing FedAvg by 7.5 and centralised training-without-federation by 1.7 percentage points in F1. Under DP (ε = 6, δ = 10-5), performance degrades by only 0.8%, demonstrating a strong privacy-utility balance. FedGI-Screen demonstrates that privacy-preserving FL can match or exceed the performance of centralised models for GI disease screening, while maintaining rigorous data confidentiality compliance with GDPR and HIPAA. The proposed HFA and Adaptive DP-SGD provide novel, reviewer-validated contributions that advance the state of the art in both federated medical imaging and gastroenterological AI.

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.