A Secure and Efficient Blockchain Enabled Federated Q-Learning Model for Vehicular Ad-Hoc Networks

Abstract

Vehicular Ad-hoc Networks (VANETs) are growing into more desirable targets for malicious individuals due to the quick rise in the number of automated vehicles around the roadside. Secure data transfer is necessary for VANETs to preserve the integrity of the entire network. Federated learning (FL) is often suggested as a safe technique for exchanging data among VANETs, however, its capacity to protect private information is constrained. This research proposes an extra level of security to Federated Q-learning by merging Blockchain technology with VANETs. Initially, traffic data is encrypted utilizing the Extended Elliptic Curve Cryptography (EX-ECC) technique to enhance the security of data. Then, the Federated Q-learning model trains the data and ensures higher privacy protection. Moreover, interplanetary file system (IPFS) technology allows Blockchain storage to improve the security of VANETs information. Additionally, the validation process of the proposed Blockchain framework is performed by utilizing a Delegated Practical Byzantine Fault Tolerance (DPBFT) based consensus algorithm. The proposed approach to federated Q-learning offered by Blockchain technology has the potential to develop VANET safety and performance. Comprehensive simulation tests are performed with several assessment criteria considered for number of vehicles 100, Throughput (102465.8 KB/s), Communication overhead (360.57 Mb), Average Latency (864.425 ms), Communication Time (19.51 s), Encryption time (0.98 ms), Decryption time (1.97 ms), Consensus delay (50 ms) and Validation delay (1.68 ms), respectively. As a result, the proposed approach performs significantly better than the existing approaches.