End-to-End Verifiable Decentralized Federated Learning

arXiv:2404.12623v1 Announce Type: new
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