About FENITH
FENITH represents a groundbreaking initiative in healthcare data analysis, leveraging federated learning techniques to enable collaborative research across Italian healthcare institutions while maintaining the highest standards of data privacy and security.
Our framework facilitates the development of sophisticated machine learning models through distributed computation, allowing healthcare providers to contribute to collective knowledge without compromising sensitive patient information.
Privacy-Preserving Architecture
Advanced federated learning protocols ensure sensitive medical data never leaves local institutions while enabling collaborative model training.
Standardized Integration
Seamless integration with existing healthcare information systems through standardized protocols and interfaces.
Research Impact
Facilitating breakthrough research in personalized medicine, rare disease detection, and treatment optimization across the Italian healthcare network.
Framework Assessment Study
Before developing FENITH, we started an ongoing and evolving study on the "Adoption of federated learning in Italian healthcare institutions" (open source: Ministry of Health). This systematic assessment will allow us to deeply understand the specific needs, challenges and opportunities within the Italian healthcare network.
Key Findings (in progress..)
Study Methodology
Qualitative Analysis
In-depth interviews with healthcare directors and research leads across Italy
Literature Review
Systematic review of federated learning implementations in healthcare
Technical Assessment
Evaluation of existing infrastructure and technical capabilities
Research Publication
Study plan of the Questionnaire for the adoption of federated learning in Italian hospitals.
Methodological guide for the analysis of innovation in the healthcare system.
In Development - Expected Q3 2025
Our research team is developing a comprehensive guide that explores the implementation of federated learning within Italian healthcare institutions. Based on our ongoing research and practical experiences, this publication will provide deep insights into:
- Privacy-Preserving Machine Learning Architectures
- Technical Implementation Guidelines
- Real-world Case Studies from Italian Healthcare Network
- Best Practices & Future Directions
Participate in Our Ongoing Research
Share your institution's perspective on federated learning adoption in healthcare
Join the StudyCurrent Research Focus
Our research initiatives leverage state-of-the-art federated learning algorithms specifically engineered for healthcare applications, adhering to GDPR compliance while maximizing model performance and privacy guarantees.
• Advanced Privacy Preservation
• Distributed Optimization
• Model Convergence
• AI Safety & Ethics
• Research Impact Metrics
Publications
Explore our key publications and technical documentation:
Technical Overview
FENITH: An Advanced Collaborative Framework for Italian Healthcare Network
📄 Download PDFProject Presentation
FENITH: A Privacy-Preserving Federated Learning Framework for Italian Healthcare Network
📄 View PresentationStrategic Analysis
Innovation Strategy in Digital Healthcare: An Integrated Framework for Federated Learning
📄 Download PDFMedia
Stay connected with FENITH through our official channels:
GitHub
Access our open-source repositories and technical documentation.
github.com/FENITH-Labs/FENITHTeam
Meet the experts behind FENITH:
Fabio Liberti
Founder and Coordinator
Leading the development of privacy-preserving federated learning solutions for healthcare networks.
Join Our Research Network
We welcome collaboration with healthcare institutions and research centers interested in advancing medical research through federated learning.
Email us at: research@fenith.org
Contact Us