Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative learning without leaking information about the local data. Unfortunately, most federated learning solutions cannot efficiently verify the execution of each participant's local machine learning model and protect the privacy of user data, simultaneously. In this article, we first propose a Zero-Knowledge Proof-based Federated Learning (ZKP-FL) scheme on blockchain. It leverages zero-knowledge proof for both the computation of local data and the aggregation of local model parameters, aiming to verify the computation process without requiring the plaintext of the local data. We further propose a Practical ZKP-FL (PZKP-FL) scheme to support fraction and non-linear operations. Specifically, we explore a Fraction-Integer mapping function, and use Taylor expansion to efficiently handle non-linear operations while maintaining the accuracy of the federated learning model. We also analyze the security of PZKP-FL. Performance analysis demonstrates that the whole running time of the PZKP-FL scheme is approximately less than one minute in parallel execution.

翻译：由于隐私泄露的担忧极大阻碍了用户参与数据共享，联邦学习逐渐成为学术界和工业界在不泄露本地数据信息的情况下实现协作学习的有前景技术。然而，大多数联邦学习方案无法同时高效验证各参与方本地机器学习模型的执行过程并保护用户数据隐私。本文首先提出一种基于区块链的零知识证明联邦学习（ZKP-FL）方案。该方案利用零知识证明同时验证本地数据计算和本地模型参数聚合过程，旨在无需获取本地数据明文即可验证计算过程。我们进一步提出实用型ZKP-FL（PZKP-FL）方案以支持分数运算和非线性操作。具体而言，我们探索了分数-整数映射函数，并利用泰勒展开高效处理非线性操作，同时保持联邦学习模型的精度。我们还分析了PZKP-FL的安全性。性能分析表明，PZKP-FL方案的并行执行总运行时间约在一分钟以内。