  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.
