Semi-supervised zero-knowledge federated learning for intelligent transportation systems

Abstract

Privacy-preserving machine learning in Vehicular Ad Hoc Networks (VANETs) must address sensitive on-board data, limited ground-truth annotations, and verifiable model-update integrity. This paper proposes ZK-FL, a decentralized federated learning framework for Intelligent Transportation Systems (ITS) scene classification that combines semi-supervised pseudo-labeling, zk-SNARK-based update validation, and Hyperledger Besu blockchain auditability. Vehicles train local models, quantize the resulting updates, and generate zero-knowledge proofs demonstrating that distributed update probes satisfy a predefined norm-boundedness constraint. Road-Side Units (RSUs) verify the proofs before aggregation and anchor the corresponding audit metadata on-chain. The framework is evaluated on the BDD100K and Mapillary Vistas v2 datasets under multiple non-IID data distributions and five aggregation methods: FedAvg, Norm Clipping, Median, Trimmed Mean, and Krum. Since Mapillary Vistas does not provide scene-level annotations, two scene-label generation strategies, including a semi-supervised pseudo-labeling approach, are introduced. Experimental results demonstrate effective scene classification, verifiable update integrity, and practical proof-generation and verification, supporting the feasibility of privacy-preserving federated learning for ITS.