Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions.

翻译：结合区块链与零知识证明（ZKP）的可验证去中心化联邦学习（FL）系统，能够使各工作节点验证本地学习与全局聚合的计算完整性。然而，此类系统尚未实现端到端可验证：数据在学习开始前仍可能被篡改。本文提出一种可验证的去中心化联邦学习系统，通过将可验证性扩展至数据源头，实现数据与计算的端到端完整性与真实性认证。针对机密性与透明性之间的固有矛盾，我们引入一种两步证明与验证（2PV）方法，并将其应用于系统核心流程：一是注册工作流，支持设备证书的非公开验证；二是学习工作流，通过非公开数据真实性证明，扩展现有基于区块链和ZKP的联邦学习系统。基于原型系统的评估表明，该方案在技术可行性上仅带来相较于现有最优方案的微小额外开销。