Latency Analysis of Consortium Blockchained Federated Learning

TL;DR

This paper proposes a decentralized federated learning system integrated with consortium blockchain for B2B scenarios, introducing a model verification mechanism and a latency model validated by experiments.

Contribution

It introduces a novel federated learning architecture with blockchain-based model verification and a latency analysis model validated through experiments.

Findings

Latency model accurately predicts system delays

Blockchain verification ensures model quality

System suitable for B2B applications

Abstract

A decentralized federated learning architecture is proposed to apply to the Businesses-to-Businesses scenarios by introducing the consortium blockchain in this paper. We introduce a model verification mechanism to ensure the quality of local models trained by participators. To analyze the latency of the system, a latency model is constructed by considering the work flow of the architecture. Finally the experiment results show that our latency model does well in quantifying the actual delays.