Review on security of federated learning and its application in healthcare

Artificial intelligence (AI) has led to a high rate of development in healthcare, and good progress has been made on many complex medical problems. However, there is a lack of patient electronic medical records standards and legal and ethical requirements for patient information privacy. The fragmentation of medical data has hindered the widespread use of AI in healthcare. Federated learning has emerged to address the problem of data fragmentation, and in combination with privacy-preserving algorithms can go a long way to address privacy concerns. Federated learning can also be combined with blockchain, edge computing, and other technologies to improve security and computational efficiency. The paper combs through recent papers on federated learning and medical industry applications, outline several architectures and classification models of federated learning, researches the application of federated learning in healthcare, and analyses the advantages of federated learning in medical applications. The paper also analyzes the security of federated learning and medical applications and analyses the various risks and attacks faced by the applications. Several standard privacy protection methods are introduced, and the current stage of the application of federated learning and healthcare is discussed. Finally, a summary and outlook on the application and security of federated learning in healthcare are presented. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Artificial intelligence (AI) has contributed to the rapid development of healthcare, addressing complex medical problems. However, the lack of standardization in patient electronic medical records and legal and ethical requirements for patient information privacy hinders widespread AI integration. Federated learning, combined with privacy-preserving algorithms, can overcome data fragmentation and improve security and computational efficiency when combined with blockchain and edge computing. This paper reviews recent research on federated learning in healthcare, explores its architectures and classification models, and analyzes its advantages and security risks in medical applications. Standard privacy protection methods are introduced and the current state of federated learning and healthcare applications is discussed, concluding with a summary and future outlook.