Federated and privacy-preserving learning in neurological imaging: a systematic review of frameworks, applications, and evidence gaps

Systematic Review of Available Models That Function as Solutions to Data Privacy in Deep Learning for Electronic Healthcare Systems

Protecting patient data in healthcare systems requires robust privacy mechanisms that balance data security with utility. This systematic review examines the application of differential privacy (DP) and the SPDZ protocol in electronic healthcare systems. The review addressed three subquestions: the current research sta...

Federated Learning for Privacy-Preserving Collaborative Data Mining in Healthcare

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 clinica...

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.

In-depth Analysis of Privacy Threats in Federated Learning for Medical Data.

Federated learning (FL) is emerging as a promising machine learning technique in the medical field for analyzing medical images, as it is considered an effective method to safeguard sensitive patient data and comply with privacy regulations. However, recent studies have revealed that the default settings of FL may inad...

Privacy-preserving methodologies against privacy attacks on deep learning: a survey

The analysis finds that noise-based methods such as DP remain the practical baseline but offer only partial protection; cryptographic approaches provide stronger theoretical guarantees at substantially higher cost; and LLM/multimodal leakage remains an urgent, under-benchmarked gap.