June 18, 2021

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Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans. (arXiv:2104.10903v2 [cs.CR] UPDATED)

Medical health care centers are envisioned as a promising paradigm to handle
the massive volume of data of COVID-19 patients using artificial intelligence
(AI). Traditionally, AI techniques often require centralized data collection
and training the model in a single organization, which is most common weakness
due to the privacy and security of raw data communication. To solve this
challenging task, we propose a blockchain-based federated learning framework
that provides collaborative data training solutions by coordinating multiple
hospitals to train and share encrypted federated models without leakage of data
privacy. The blockchain ledger technology provides the decentralization of
federated learning model without any central server. The proposed homomorphic
encryption scheme encrypts and decrypts the gradients of model to preserve the
privacy. More precisely, the proposed framework: i) train the local model by a
novel capsule network to segmentation and classify COVID-19 images, ii) then
use the homomorphic encryption scheme to secure the local model that encrypts
and decrypts the gradients, and finally the model is shared over a
decentralized platform through proposed blockchain-based federated learning
algorithm. The integration of blockchain and federated learning leads to a new
paradigm for medical image data sharing in the decentralized network. The
conducted experimental resultsdemonstrate the performance of the proposed
scheme.