End-to-End Verifiable Decentralized Federated Learning
<|reference_start|>End-to-End Verifiable Decentralized Federated Learning: 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.<|reference_end|>
@article{lee2024end-to-end,
    title={End-to-End Verifiable Decentralized Federated Learning},
    author={Chaehyeon Lee, Jonathan Heiss, Stefan Tai, and James Won-Ki Hong},
    journal={arXiv preprint arXiv:2404.12623},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12623},
    primaryClass={cs.LG cs.CR cs.DC}
}