Elliptic Crypt With Secured Blockchain Assisted Federated Q-Learning Framework for Smart Healthcare

In this paper, a novel Elliptic Crypt with Secured Blockchain-backed Federated Q-Learning Framework is proposed to offer an intelligent healthcare system that mitigates the attacks and data misused by malicious intruders. Initially, the entered IoMT data is collected from publicly available datasets and encrypted using the Extended Elliptic Curve Cryptography (E_ECurCrypt) technique for ensuring the security. This encrypted data is fed as an input to the blockchain-powered collaborative learning model. Here, the federated Q-learning model trains the inputs and analyzes the presented attacks to ensure better privacy protection. Afterwards, the data is securely stored in decentralized blockchain technology. Subsequently, an effective Delegated Proof of Stake (Del_PoS) consensus algorithm is used to validate the proposed framework. The experiment is conducted using the WUSTL-EHMS-2020 dataset and the performances are analyzed by evaluating multiple matrices and compared to other existing methods. The performance of the proposed framework can be assessed using multiple matrices and the results will be compared to other existing methods. As a result, the proposed method has achieved 99.23% accuracy, 98.42% precision, 98.12% recall, 98.27% F1 score, 59080.506 average throughput, 59080.506 average decryption time 1.94 seconds and an average encryption time of 1.84 seconds and are superior to conventional methods.