A Zero-Knowledge-Based Approach to Resist Poisoning Attacks in Federated Learning

Abstract

In the Internet of Vehicles (IoV) network, numerous vehicle terminals are required to continuously upload local data to maintain the latest service models, which supports intelligent transportation and personalized services. However, the privacy risks posed by this continuous data uploading cannot be ignored. Federated learning, as a distributed ma-chine learning paradigm, enables global model training without sharing original data. The introduction of blockchain further supports decentralization and immutability. However, federated learning also faces the risk of poisoning attacks, where malicious clients may upload abnormal or tampered model updates, severely impacting global model performance. To address this, this paper proposes a security framework that combines zero-knowledge proofs, federated learning, and blockchain. Clients use zero-knowledge proofs to ensure the legitimacy of uploaded updates, while the blockchain is responsible for verification and storage. Ultimately, a robust global model is obtained through federated aggregation. Experimental results demonstrate that this scheme effectively resists poisoning attacks, significantly improving system security and reliability while protecting user privacy.