Federated Learning and Reputation-Based Node Selection Scheme for Internet of Vehicles

【Author】 Su, Zhaoyu; Cheng, Ruimin; Li, Chunhai; Chen, Mingfeng; Zhu, Jiangnan; Long, Yan

【Source】ELECTRONICS

【影响因子】2.690

【Abstract】With the rapid development of in-vehicle communication technology, the Internet of Vehicles (IoV) is gradually becoming a core component of next-generation transportation networks. However, ensuring the activity and reliability of IoV nodes remains a critical challenge. The emergence of blockchain technology offers new solutions to the problem of node selection in IoV. Nevertheless, traditional blockchain networks may suffer from malicious nodes, which pose security threats and disrupt normal blockchain operations. To address the issues of low participation and security risks among IoV nodes, this paper proposes a federated learning (FL) scheme based on blockchain and reputation value changes. This scheme encourages active involvement in blockchain consensus and facilitates the selection of trustworthy and reliable IoV nodes. First, we avoid conflicts between computing power for training and consensus by constructing state-channel transitions to move training tasks off-chain. Task rewards are then distributed to participating miner nodes based on their contributions to the FL model. Second, a reputation mechanism is designed to measure the reliability of participating nodes in FL, and a Proof of Contribution Consensus (PoCC) algorithm is proposed to allocate node incentives and package blockchain transactions. Finally, experimental results demonstrate that the proposed incentive mechanism enhances node participation in training and successfully identifies trustworthy nodes.

【Keywords】Internet of Vehicles (IoV); blockchain; node selection; federated learning; consensus mechanism

【发表时间】2025 JAN

【收录时间】2025-02-23

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