BZ-BFT: An Efficient and Scalable Consensus Mechanism for Blockchain Federated Learning
Hao-Tse Chung, Shao‐Hung Cheng, Yu‐Jia Chen, Li‐Chun Wang · 2025
Emerging Blockchain-empowered Federated Learning (BCFL) technology combines the decentralized security of blockchain with the privacy protection of federated learning. BCFL addresses the issue of single points of failure in centralized systems, making it an increasingly popular solution. However, current consensus mechanisms, such as Proof of Work (PoW), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT), lead to challenges such as high computational costs and limited scalability. This paper proposes a Batch Zero-Knowledge Proof-based practical Byzantine fault-tolerant (BZ-BFT) consensus mechanism for BCFL to enhance efficiency and reliability. By integrating Zero-Knowledge Proof (ZKP), our approach enables the verification of the primary node's proposal without revealing information from other network nodes, thereby ensuring the credibility of the aggregated results. To address the high computational overhead associated with ZKP, we present a batch quantization preprocessing technique called BatchZKP. Our proposed BZ-BFT reduces initialization, proof generation, and verification time by$97.81 \%, 70.0 \%$, and 47.64 %, respectively, significantly boosting BCFL system efficiency and reliability. Additionally, our approach reduces communication complexity from$O\left(n^{2}\right)$to$O(n)$and enhances Byzantine fault tolerance to${1/2}$.