Carchain: A Blockchain-Based Federated Learning Optimization Scheme in Autonomous-Driving Scenario

The aggregation process inherent in federated learning lacks auditability, rendering it susceptible to the influence of malicious nodes that may upload compromised models, thereby undermining the accuracy of the global model or potentially preventing successful aggregation altogether. In response to these challenges, this paper introduces “carchain”, a blockchain-based ecosystem designed to counteract poisoning and deception attacks within federated learning, while also establishing a framework of trust and incentives. Carchain employs a committee consensus mechanism to verify the accuracy of model updates off-chain. Additionally, it incorporates a reputation calculation mechanism that assesses the quality of model updates, coupled with an incentive structure based on reputation values to motivate the participation of high-quality nodes in the federated learning process. The results of experment indicate that carchain significantly enhances the security and efficiency of the system: when subjected to an attack involving 50% malicious trainers, the final model's loss is reduced to 86.9% compared to that of the BLFC methods; under an attack involving 50% malicious evaluators, the final model's loss is only 25.2% of that observed with direct detection methods. Additionally, the new incentive mechanism allows participants who contribute more data to earn additional tokens, thereby increasing motivation. Lastly, the storage overhead associated with the blockchain is reduced to 50.6% of that required by a single-chain structure.