Privacy-Preserving Cross-Domain Score Fusion for Distributed Public-Service Data: A Federated Architecture and Empirical Benchmark

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...

A Federated Learning Framework with Differential Privacy for High-Accuracy and Privacy-Preserving Healthcare Prediction

This work demonstrates that strong privacy protection and state-of-the-art prediction performance are not mutually exclusive, thereby offering a practical and scalable solution for collaborative healthcare analytics.

Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis

This work proposes a federated inference framework in which several commercial LLM APIs collaborate without requiring access to raw student data or proprietary model internals, and conducts rigorous privacy-utility analysis showing strong privacy guarantees with minimal accuracy loss.

A privacy preserving federated meta ensemble stacking framework with explainability and differential privacy for chronic kidney disease prediction in resource constrained settings

Findings demonstrate that the proposed framework provides an accurate, privacy-aware, and interpretable solution for decentralized CKD prediction, and maintains robust performance under Gaussian noise.

Privacy-Preserving Cross-Domain Data Mining Modeling Method Based on Federated Learning

This paper proposes a privacy-preserving modeling method for cross-domain data mining based on federated learning. Building on the FedAvg framework, the method integrates cross-domain data node modeling, heterogeneous feature semantic mapping, distribution-shift-aware aggregation, and dynamic gradient perturbation into...