Federated Learning for Secure and Privacy-Preserving Medical Collaboration Across Multi-Cloud Healthcare Systems

View Full Paper

Ask AI

Bookmark

Authors

Discussion

Federated learning enhances data confidentiality in medical collaboration across multi-cloud systems, suggesting improved privacy preservation.

Federated learning improves data confidentiality while enabling medical collaboration across decentralized systems.
The model achieves accuracy comparable to centralized storage, minimizing risks associated with data transmission.
This analysis leverages real-world EHR data to evaluate performance, scalability, and security in medical settings.
The findings indicate a potential for federated learning to facilitate privacy-preserving AI in regulated healthcare environments.

Ejaz et al. (2024) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

Federated learning enhances data confidentiality in medical collaboration across multi-cloud systems, suggesting improved privacy preservation.

Federated learning improves data confidentiality while enabling medical collaboration across decentralized systems.
The model achieves accuracy comparable to centralized storage, minimizing risks associated with data transmission.
This analysis leverages real-world EHR data to evaluate performance, scalability, and security in medical settings.
The findings indicate a potential for federated learning to facilitate privacy-preserving AI in regulated healthcare environments.

Ejaz et al. (2024) studied this question.

View Full Paper

Ask AI

Bookmark