Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images.

ABSTRACT: Medical healthcare centers are envisioned as a promising paradigm to handle the massive volume of data for COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and training models within a single organization. This practice can be considered a weakness as it leads to several privacy and security concerns related to raw data communication. To overcome this weakness and secure raw data communication, we propose a blockchain-based federated learning framework that provides a solution for collaborative data training. The proposed framework enables the coordination of multiple hospitals to train and share encrypted federated models while preserving data privacy. Blockchain ledger technology provides decentralization of federated

SUBMITTER: Kumar R 

PROVIDER: S-EPMC9632244 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature