End-to-End Verifiable Decentralized Federated Learning

• URL: http://arxiv.org/abs/2404.12623v1
• Date: Fri, 19 Apr 2024 04:43:01 GMT
• Title: End-to-End Verifiable Decentralized Federated Learning
• Authors: Chaehyeon Lee, Jonathan Heiss, Stefan Tai, James Won-Ki Hong,
• Abstract summary: Verifiable decentralized federated learning (FL) systems combine blockchains and zero-knowledge proofs (ZKP) We propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and extending verifiability to the data source.
• Score: 1.374949083138427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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