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End-to-End Verifiable Decentralized Federated Learning
April 22, 2024, 4:41 a.m. | Chaehyeon Lee, Jonathan Heiss, Stefan Tai, James Won-Ki Hong
cs.LG updates on arXiv.org arxiv.org
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 …
abstract aggregation arxiv blockchains computational cs.cr cs.dc cs.lg data decentralized federated learning global however integrity knowledge paper prior systems type workers
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