A Novel Framework for Differential Privacy based Federated Continual Learning for Dynamic Medical Data Analysis

Federated Learning for Privacy-Preserving Collaborative Data Mining in Healthcare

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 Federated Learning Framework for Cardiovascular Disease Risk Prediction under Non-IID Data

This work designs a privacy-compliant federated learning architecture to realize clinical heart disease risk prediction, and the FedAvg algorithm will be used to enable cross-institutional collaborative training without sharing raw patient data to provide a feasible privacy-preserving solution for cross-hospital clinic...

Decentralized Machine Learning for Healthcare: Survey on Disease Prognosis and Secure Communication Frameworks

One of the biggest challenges in implementing AI in healthcare is the fragmentation of data across hospitals, as well as privacy regulations. Federated learning tackles these challenges by allowing models to be collaboratively built, while patient data is kept at its source. The survey focuses on two primary medical fi...

Federated Self-Supervised Learning for Privacy-Preserving Clinical Diagnostics in Distributed Healthcare Systems

The proposed FSSL framework provides a scalable foundation for privacy-conscious collaborative clinical AI while keeping patient data within the originating healthcare institution and is intended to support, rather than replace, professional clinical decision-making.

FEDERATED LEARNING-BASED PRIVACY PRESERVATION FOR INTELLIGENT DATA ANALYTICS

The authors discussed the potential of federated learning in intelligent data analytics with privacy, and reported the potential advantages of federated learning in the context of secure and intelligent data analysis, while preserving data privacy.