Blockchain enabled federated learning for privacy preserving medical data processing in healthcare as a systematic literature review
TL;DR
A PRISMA-based Systematic Literature Review was conducted to synthesize evidence from 37 peer-reviewed studies focusing on blockchain-enabled federated learning for healthcare data privacy, security, and preservation, indicating that the integration of blockchain and federated learning offers significant potential for secure and privacy-preserving healthcare data management.
Abstract
Healthcare organizations use the combination of blockchain technology with Federated Learning to improve their data security and privacy protection systems. Research studies on FL and blockchain technology have increased in number yet only two Systematic Literature Reviews (SLRs) exist which examine their joint use in protecting medical patient information. To reduce this limitation, a PRISMA-based Systematic Literature Review (SLR) was conducted to synthesize evidence from 37 peer-reviewed studies focusing on blockchain-enabled federated learning for healthcare data privacy, security, and preservation. The survey presents its findings by showing how the combination of blockchain’s unchangeable record system with federated learning’s decentralized data processing methods protects patient information from both unauthorized access and data alteration and public record access threats. The review also discusses emerging encryption approaches reported in the literature, including XChaCha20, as a potential future direction for improving efficiency and scalability in privacypreserving healthcare systems. The combination of blockchain technology with FL enables to build trust between organizations while using encrypted patient information for training machine learning algorithms. The publication of research documents shows an increasing trend in the adoption of Blockchain-enabled FL (BFL) technology within the healthcare sector. This systematic review of BFL covers the topics of the BFL-related research, its pros and cons, and thus to help the security working on the development of security frameworks that focus on preservation. By providing a chart of current methods, pinpointing preservation problems, and describing future research directions such as XChaCha20 integration, the SLR gives practicable hints for secure, preservationcentric healthcare systems. The findings indicate that the integration of blockchain and federated learning offers significant potential for secure and privacy-preserving healthcare data management. Furthermore, emerging encryption approaches such as XChaCha20 may represent promising directions for future research, although additional empirical validation is required.