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

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

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, demonstrating strong potential for deployment in multi-hospital clinical environments.

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.

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-Enabled Image Processing for Privacy-Preserving Gastrointestinal Disease Screening

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.

Privacy-Preserving and Quantum-Resistant Federated Learning Framework for Fog Healthcare Systems

Quantum computing is on the horizon and will destroy existing cryptography standards, putting digital healthcare system security and patient privacy at danger. For very private and secure communication in fog healthcare settings, this article presents a new Quantum-Resistant Federated Deep Learning (QR-FDL) Framework. The QR-FDL architecture incorporates a lattice-based technique (like Kyber) into the model aggregation stage of Federated Learning (FL), which is a kind of Post-Quantum Cryptography (PQC). We provide a solution that uses Differential Privacy (DP) to prevent attacks based on gradients in data reconstruction and guarantees quantum-era security for weights of models with gradients sent between the central server and decentralized fog nodes. The practical use of QR-FDL is shown by its rapid convergence rate and excellent classification accuracy of 91.5% in an empirical assessment including a medical imaging job, such as tumor classification. Critically, we measure the cryptographic overhead and demonstrate that, even if PQC causes a regulated latency rise the total FL round time is still tolerable for real-time fog settings. In order to implement quantum-secure along with privacy-preserving Deep Learning in mission-critical healthcare communications, this study presents the first proven, end-to-end solution. In addition to showing that federated medical facilities can be securely encrypted from end to end, this architecture uses differential privacy during model training to protect patients’ personal information. Through the research, we demonstrate its practical implementation in the near future for it is hard to balance the precision of modeled obstructions with cryptographic overhead that still remains acceptable. The results of this research lay the groundwork for medical AI systems of the future that are both secure and respectful of patients’ privacy.

TDC-FL: a trust-aware and cluster-driven federated learning framework for heterogeneous healthcare systems