Federated Learning for Privacy-Preserving Collaborative Data Mining in Healthcare

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...

FedDeepRiskNet++: a privacy-enhanced federated learning framework for secure and scalable medical IoT systems

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...

FedE: Protecting Training Data of Federated Learning Based on Multi-Precision Functional Encryption

FedE, a multi-precision, multi-source, heterogeneous privacy-preserving federated learning training method based on functional encryption that enhances numerical adaptation during ciphertext computation and prevents model parameter updates from easily compromising privacy in cross-institutional federated learning.

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...

Information Security and Federated Learning for Collaborative Healthcare Diagnosis

A federated learning framework with data locality for healthcare diagnostics that enables multiple hospitals to jointly train accurate models while keeping patient data within institutional boundaries is introduced.