AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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...
The results demonstrate that strong privacy protection, communication efficiency, and high diagnostic reliability can coexist within large-scale federated healthcare systems, making FedDeepRiskNet + + a practical solution for next-generation medical IoT systems.
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, 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.
A holistic framework, MedPFL, is proposed for analyzing privacy risks in processing medical data in the FL environment and developing effective mitigation strategies for protecting privacy, which demonstrates the higher privacy risks in FL to process medical images.
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.
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