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Medical health care centers are envisioned as a promising paradigm to handlethe massive volume of data of COVID-19 patients using artificial intelligence(AI). Traditionally, AI techniques often require centralized data collectionand training the model in a single organization, which is most common weaknessdue to the privacy and security of raw data communication. To solve thischallenging task, we propose a blockchain-based federated learning frameworkthat provides collaborative data training solutions by coordinating multiplehospitals to train and share encrypted federated models without leakage of dataprivacy. The blockchain ledger technology provides the decentralization offederated learning model without any central server. The proposed homomorphicencryption scheme encrypts and decrypts the gradients of model to preserve theprivacy. More precisely, the proposed framework: i) train the local model by anovel capsule network to segmentation and classify COVID-19 images, ii) thenuse the homomorphic encryption scheme to secure the local model that encryptsand decrypts the gradients, and finally the model is shared over adecentralized platform through proposed blockchain-based federated learningalgorithm. The integration of blockchain and federated learning leads to a newparadigm for medical image data sharing in the decentralized network. Theconducted experimental resultsdemonstrate the performance of the proposedscheme.

Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans