Privacy-preserving and scalable federated blockchain scheme for healthcare 4.0

IoT plays a significant role in the growth of clinical data for identifying hazardous diseases and building drugs for patient diagnosis and medical care. Blockchain and federated learning are widely proposed in existing studies for localized data training and storage in a secure, decentralized environment. However, several studies rely on the federated averaging method in federated learning, which introduces high energy consumption during several training rounds. Frequent transactions for storing patient records in blockchain and smart contract processing present network congestion challenges. In this paper, we propose a privacy-preserving federated learning and scalable blockchain scheme. First, we present a satisfaction scoring method for model aggregation to improve energy efficiency during the federated learning process. Secondly, we design an Ethereum-based blockchain network with sidechains to process smart contract transactions separately and reduce computation overload in the blockchain mainchain. Evaluation of the proposed scheme is compared with the baseline federated average method and transaction processing of smart contracts with the Ethereum main chain. Results demonstrate reduced CPU consumption using the proposed model aggregation method, an improvement over the federated average method, and an improvement of 43.65 % in transaction processing speed using two sidechains ensuring side.