Paper Contents
Abstract
Federated machine learning that ensures privacy is a critical tool for protecting sensitive information about health while promoting cooperative model building amongst different health care organizations. Traditional central FL may not be advantageous for private health information because it increases points of failure, bottlenecks of communication, and even incidences of data leakage. Recent developments that mitigate these constraints are decentralized FL frameworks that offer better privacy and security. Two approaches that enhance communication efficiency without compromising data integrity are ring-based structure and Ring-All reduce-based data sharing. The privacy-preserving FL architecture further leverages FAIRfindable, accessible, interoperable, and reusablehealth data for safe model training without explicitly sharing data among collaborators. These approaches have been found to be efficient in predicting the risk of readmission. The related advances include federated edge and privacy-enhancing technology federated edge intelligence frameworks for medical images, which provide additional data safety guarantees and mitigate privacy concerns of IoHT-based healthcare systems. Several possibilities of safe data management and incentive schemes are provided by blockchain and NFT technologies for federated learning frameworks. These advances are well driving the construction of safe, effective, and private machine learning models with strict privacy and regulatory requirements.
Copyright
Copyright © 2024 SIGILIPALLI LAXMIPRASANNA. This is an open access article distributed under the Creative Commons Attribution License.
