Privacy-Preserving Federated Learning and Analytics with FLAME

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.

Starfish-FL: Harnessing Agentic Federated Analytics

Federated learning (FL) enables privacy-preserving multi-site clinical research, but orchestrating, monitoring, and interpreting federated analyses remains out of reach for the biostatisticians and clinicians who most need them. We present Starfish-FL, a multi-tier agent harness for cross-silo healthcare analytics buil...

Fedflow: cloud orchestration for federated learning with the FeatureCloud platform

fedflow, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud, is introduced, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud that enables automation of multi-client FL tasks, facilitates embedding of FeatureCloud in standard bioinformatics...

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.

A Federated Learning Framework for Privacy-Preserving Oral Cancer Screening on Smartphones

This work presents a practical FL framework that supports geographically distributed collaboration among AI healthcare researchers and facilitates the development of robust models for oral cancer screening.

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