Federated learning (FL) is transforming healthcare by allowing institutions to collaboratively train AI models without sharing sensitive patient data. This approach enhances data privacy and security, aligning with regulations like GDPR and HIPAA. For instance, a study introduced a Deep Federated Learning (DFL) framework tailored for IoT-based systems, achieving an impressive 97% accuracy in skin disease detection while preserving data privacy. mdpi.com
In medical imaging, FL enables hospitals to collaboratively train models for detecting diseases such as cancer or retinal disorders using X-rays, MRIs, and other imaging data. This collaborative approach has led to significant improvements in diagnostic accuracy. Additionally, integrating FL with blockchain technology has resulted in secure architectures for smart healthcare solutions, ensuring data privacy and security. mdpi.com
Key Takeaways
- FL allows secure, collaborative AI model training without sharing patient data.
- A DFL framework achieved 97% accuracy in skin disease detection while preserving privacy.
- FL enables collaborative training of medical imaging models for disease detection.
- Integrating FL with blockchain ensures data privacy and security in smart healthcare solutions.