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

【Author】 Kumar, Rajesh; Kumar, Jay; Khan, Abdullah Aman; Zakria; Ali, Hub; Bernard, Cobbinah M.; Khan, Riaz Ullah; Zeng, Shaoning

【Source】COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

【影响因子】7.422

【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 learning models without relying on a central server. Moreover, the proposed homomorphic encryption scheme encrypts and decrypts the gradients of the model to preserve privacy. More precisely, the proposed framework: (i) train the local model by a novel capsule network for segmentation and classification of COVID-19 images, (ii) furthermore, we use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, (iii) finally, the model is shared over a decentralized platform through the proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing over the decentralized network. To validate our proposed model, we conducted comprehensive experiments and the results demonstrate the superior performance of the proposed scheme.

【Keywords】COVID-19; Privacy-preserved; Federated-learning; Blockchain

【发表时间】2022 DEC

【收录时间】2023-01-14

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-联邦学习