Blockchain-Based Trustworthy Verifiable Federated Learning for 6G Internet of Vehicles

Abstract

Within the realm of 6G Internet of Vehicles (6G-IoV), Federated Learning (FL) has become a notable machine learning framework, providing a decentralized method to protect data privacy while allowing cooperative model training. Specifically, with 6G technology, FL will benefit from ultra-low latency, high reliability and massive connectivity, enabling real-time model updates and efficient data sharing in the 6G-IoV ecosystem. However, FL faces challenges like the single points of failure and potential privacy leakage from data providers. To tackle the aforementioned challenges, we propose a blockchain-based trustworthy verifiable FL scheme for 6G-IoV, that is, AVBFL. Firstly, we introduce blockchain technology to address the issue of decentralization by storing transactions on-chain. Furthermore, to protect the privacy of local gradients, we utilize the Burmester-Desmedt (BD) multi-party key agreement protocol to negotiate a shared key and encrypt the gradients with the AES encryption algorithm. We also sign transactions using the ECDSA signature algorithm. Additionally, we design a time-sensitive Proof of Stake (TPoS) consensus mechanism based on Newton’s cooling law to boost participants’ enthusiasm for training and select the miner with the highest stake to mine the block. Finally, experiments have demonstrated the effectiveness of AVBFL. In the presence of malicious nodes, the average accuracy rate is increased by 71.8% compared to the VFL scheme and by 8.6% compared to the VBFL scheme.